import sys # we require code from other folders
import pandas as pd
import numpy as np
import itertools
import pickle
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'DeepMoA: method to predict the mechanism of action of cancer drugs
Select data and import libraries
import seaborn as sns
import matplotlib.pyplot as plt
CB_color_cycle = ['#EECC16', '#62BB35', '#FDAE33','#208EA3', '#EA4E9D', '#984ea3','#999999', '#e41a1c', '#dede00']
#sns.set_style("darkgrid")import matplotlib.font_manager as fm
font_files = fm.findSystemFonts()
plt.rcdefaults()
# Go through and add each to Matplotlib's font cache.
for font_file in font_files:
fm.fontManager.addfont(font_file)
plt.rc('font', family='Roboto')plt.rc('font', family='Roboto')
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Roboto'#%config InlineBackend.figure_format='retina'# pytorch relates imports
import torch
import torch.nn as nn
import torch.optim as optim
# imports from captum library
from captum.attr import LayerDeepLift# for combobox
from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgetsimport plotly.express as px
import plotly.graph_objects as go
import plotly.io as piopd.options.display.min_rows = 20000
pd.set_option('max_colwidth', 200)pd.options.display.max_rows = 20000pd.set_option('min_rows', 20000)mac = "/Users/katyna/Library/CloudStorage/OneDrive-Tecnun/"
windows = "C:/Users/ksada/OneDrive - Tecnun/"
computer = windows # CHANGEsys.path.append(computer + "SparseGO_code/code")
import util
from util import *%matplotlib inline#%matplotlib inline
# To make histograms
def histogram(dataframe, color, title, ylabel,n_bins):
N, bins, patches = plt.hist(dataframe, color=color,bins=n_bins, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]<0.05:
patches[i].set_facecolor(CB_color_cycle[2])
plt.xlabel("P-value", fontsize=16)
plt.ylabel(ylabel, fontsize=16)
plt.title(title, fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
ax = plt.subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False) inputdir = computer+"SparseGO_code/data/cross_validation_expression/allsamples/" # CHANGE
dir1=computer+"Tesis/Codigo/VariableImportance/"
dir2=computer+"SparseGO_code/results/weights&biases/Expression_MSE_all/" # CHANGE
resultsdir=dir2gene2id = inputdir+"gene2ind.txt"
cell2id=inputdir+"cell2ind.txt"
drug2id=inputdir+"drug2ind.txt"
drug2fingerprint=inputdir+"drug2fingerprint.txt"
load=resultsdir+"last_model.pt"
onto = inputdir+"ontology.txt" # CHANGE
genotype=inputdir+"cell2expression.txt" # CHANGE
num_neurons_per_GO = 6 # CHANGEDeepLIFT
gene2id_mapping = load_mapping(gene2id)
dG, terms_pairs, genes_terms_pairs = load_ontology(onto, gene2id_mapping)
sorted_pairs, level_list, level_number = sort_pairs(genes_terms_pairs, terms_pairs, dG, gene2id_mapping)
layer_connections = pairs_in_layers(sorted_pairs, level_list, level_number)
cell_features = np.genfromtxt(genotype, delimiter=',')
drug_features = np.genfromtxt(drug2fingerprint, delimiter=',')
drug2id_mapping = load_mapping(drug2id)
cell2id_mapping = load_mapping(cell2id)
num_genes = len(gene2id_mapping)
drug_dim = len(drug_features[0,:])There are 15015 genes
There are 1 roots: GO:0008150
There are 4184 terms
There are 1 connected components
model = torch.load(load, map_location='cuda:%d' % 0)modelsparseGO_nn(
(genes_terms_sparse_linear_1): SparseLinearNew(
in_features=15015, out_features=25104, bias=True, sparsity=0.0030196221878822263, connectivity=tensor([[ 0, 1, 2, ..., 23721, 23722, 23723],
[ 0, 0, 0, ..., 15014, 15014, 15014]], device='cuda:0'), small_world=False
)
(genes_terms_tanh): Tanh()
(genes_terms_batchnorm): BatchNorm1d(25104, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(GO_terms_sparse_linear_1): SparseLinearNew(
in_features=25104, out_features=8304, bias=True, sparsity=0.002372788160788691, connectivity=tensor([[ 966, 967, 968, ..., 7047, 7048, 7049],
[ 0, 0, 0, ..., 25103, 25103, 25103]], device='cuda:0'), small_world=False
)
(GO_terms_tanh_1): Tanh()
(GO_terms_batchnorm_1): BatchNorm1d(8304, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(GO_terms_sparse_linear_2): SparseLinearNew(
in_features=8304, out_features=3684, bias=True, sparsity=0.003911619061964564, connectivity=tensor([[ 0, 1, 2, ..., 3681, 3682, 3683],
[ 0, 0, 0, ..., 8303, 8303, 8303]], device='cuda:0'), small_world=False
)
(GO_terms_tanh_2): Tanh()
(GO_terms_batchnorm_2): BatchNorm1d(3684, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(GO_terms_sparse_linear_3): SparseLinearNew(
in_features=3684, out_features=1650, bias=True, sparsity=0.007924193070772875, connectivity=tensor([[ 150, 151, 152, ..., 1641, 1642, 1643],
[ 0, 0, 0, ..., 3683, 3683, 3683]], device='cuda:0'), small_world=False
)
(GO_terms_tanh_3): Tanh()
(GO_terms_batchnorm_3): BatchNorm1d(1650, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(GO_terms_sparse_linear_4): SparseLinearNew(
in_features=1650, out_features=726, bias=True, sparsity=0.015807663410969196, connectivity=tensor([[ 474, 475, 476, ..., 711, 712, 713],
[ 0, 0, 0, ..., 1649, 1649, 1649]], device='cuda:0'), small_world=False
)
(GO_terms_tanh_4): Tanh()
(GO_terms_batchnorm_4): BatchNorm1d(726, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(GO_terms_sparse_linear_5): SparseLinearNew(
in_features=726, out_features=318, bias=True, sparsity=0.03305785123966942, connectivity=tensor([[ 60, 61, 62, ..., 105, 106, 107],
[ 0, 0, 0, ..., 725, 725, 725]], device='cuda:0'), small_world=False
)
(GO_terms_tanh_5): Tanh()
(GO_terms_batchnorm_5): BatchNorm1d(318, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(GO_terms_sparse_linear_6): SparseLinearNew(
in_features=318, out_features=120, bias=True, sparsity=0.06981132075471698, connectivity=tensor([[ 0, 1, 2, ..., 93, 94, 95],
[ 0, 0, 0, ..., 317, 317, 317]], device='cuda:0'), small_world=False
)
(GO_terms_tanh_6): Tanh()
(GO_terms_batchnorm_6): BatchNorm1d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(GO_terms_sparse_linear_7): SparseLinearNew(
in_features=120, out_features=42, bias=True, sparsity=0.2, connectivity=tensor([[ 18, 19, 20, ..., 21, 22, 23],
[ 0, 0, 0, ..., 119, 119, 119]], device='cuda:0'), small_world=False
)
(GO_terms_tanh_7): Tanh()
(GO_terms_batchnorm_7): BatchNorm1d(42, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(GO_terms_sparse_linear_8): SparseLinearNew(
in_features=42, out_features=30, bias=True, sparsity=1.0, connectivity=tensor([[ 0, 1, 2, ..., 27, 28, 29],
[ 0, 0, 0, ..., 41, 41, 41]], device='cuda:0'), small_world=False
)
(GO_terms_tanh_8): Tanh()
(GO_terms_batchnorm_8): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(drug_linear_layer_1): Linear(in_features=2048, out_features=200, bias=True)
(drug_tanh_1): Tanh()
(drug_batchnorm_layer_1): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(drug_linear_layer_2): Linear(in_features=200, out_features=100, bias=True)
(drug_tanh_2): Tanh()
(drug_batchnorm_layer_2): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(drug_linear_layer_3): Linear(in_features=100, out_features=50, bias=True)
(drug_tanh_3): Tanh()
(drug_batchnorm_layer_3): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(final_linear_layer): Linear(in_features=80, out_features=40, bias=True)
(final_tanh): Tanh()
(final_batchnorm_layer): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(final_aux_linear_layer): Linear(in_features=40, out_features=1, bias=True)
(final_aux_tanh): Tanh()
(final_linear_layer_output): Linear(in_features=1, out_features=1, bias=True)
)
# Save layers to be analyzed
model_layers = []
model_layers.append(model.genes_terms_sparse_linear_1)
model_layers.append(model.GO_terms_sparse_linear_1)
model_layers.append(model.GO_terms_sparse_linear_2)
model_layers.append(model.GO_terms_sparse_linear_3)
model_layers.append(model.GO_terms_sparse_linear_4)
model_layers.append(model.GO_terms_sparse_linear_5)
model_layers.append(model.GO_terms_sparse_linear_6)
model_layers.append(model.GO_terms_sparse_linear_7)GO terms info
# Go term names
gene_ontology = pd.read_excel('all_go_terms_info.xlsx')Get all layers’ GO term with the neuron number
all_terms_ids = {}
all_terms_names = {}
all_layers_non_virtual = {} # store only terms that are part of the layer (remove virtual), those are the important attribuitions
all_layers_non_virtual_names = {}
num_neurons_per_GO = 6
for layer_number in range(len(layer_connections)-1):
layer_pairs = layer_connections[layer_number]
terms_ids = []
names = []
output_id = create_index(layer_pairs[:,0]) # first 6 neurons correspond to the term with key 0
for term in output_id.keys():
#name = gene_ontology.loc[gene_ontology['GO_term'] == term].to_numpy()[0,3].replace("_"," ").capitalize()
name = gene_ontology.loc[gene_ontology['id'] == term].to_numpy()[0,1].capitalize()
for i in range(1,num_neurons_per_GO+1): # vector que tiene GO:0000038_1, GO:0000038_2 ... GO:0000038_6 y asi luego concatenar con las attributions
terms_ids.append(term+"_"+str(i))
names.append(name+" ("+str(i)+")")
all_terms_ids[layer_number] = np.array(terms_ids)
all_terms_names[layer_number] = np.array(names)
non_virtual = [] # store the terms part of that layer
non_virtual_names = []
for term in level_list[layer_number+1]:
nv_name = gene_ontology.loc[gene_ontology['id'] == term].to_numpy()[0,1].capitalize()
for i in range(1,7):
non_virtual.append(term+"_"+str(i))
non_virtual_names.append(nv_name+" ("+str(i)+")")
all_layers_non_virtual[layer_number] = non_virtual
all_layers_non_virtual_names[layer_number] = non_virtual_namesAll GO terms part of a layer (non-virtual) with their corresponding name and layer number…
real_go_info = pd.DataFrame({"GO_term":[],"Name":[],"layer_number":[]})
for layer_number in range(len(layer_connections)-1):
layer_go_info = pd.DataFrame({"GO_term":all_layers_non_virtual[layer_number],"Name":all_layers_non_virtual_names[layer_number],"layer_number":(layer_number)})
real_go_info = pd.concat((real_go_info,layer_go_info))
real_go_info.head()| GO_term | Name | layer_number | |
|---|---|---|---|
| 0 | GO:0000019_1 | Regulation of mitotic recombination (1) | 0.0 |
| 1 | GO:0000019_2 | Regulation of mitotic recombination (2) | 0.0 |
| 2 | GO:0000019_3 | Regulation of mitotic recombination (3) | 0.0 |
| 3 | GO:0000019_4 | Regulation of mitotic recombination (4) | 0.0 |
| 4 | GO:0000019_5 | Regulation of mitotic recombination (5) | 0.0 |
Drugs info
def get_compound_names(file_name):
compounds = []
with open(file_name, 'r') as fi:
for line in fi:
tokens = line.strip().split('\t')
compounds.append([tokens[1],tokens[2]])
return compoundsdrugs = get_compound_names(inputdir+"compound_names.txt")
drugs.pop(0)['SMILE', 'Name']
DeepLIFT for VNN
Reference activation… (baseline)
median_cell_features = np.median(cell_features,axis=0) # to use as a reference
median_drug_features = np.genfromtxt(computer+"SparseGO_code/data/glucose_fingerprint.txt", delimiter=',')Attribution function: sum
def get_layer_attribution(layer_number,input_data,baseline,selected_drug_data):
dl = LayerDeepLift(model, model_layers[layer_number],multiply_by_inputs = True) # CHOOSE LAYER TO STUDY
dl_attr_test = dl.attribute(input_data,baseline)
dl_attr_test_sum = dl_attr_test.cpu().detach().numpy().sum(0) # se suman las attributions para cada sample
attribution_data = pd.DataFrame(np.column_stack((all_terms_ids[layer_number],dl_attr_test_sum)), columns=["GO_term",selected_drug_data[1]])
attribution_data[[selected_drug_data[1]]] = attribution_data[[selected_drug_data[1]]].apply(pd.to_numeric).round(10)
attribution_data = attribution_data.loc[attribution_data['GO_term'].isin(all_layers_non_virtual[layer_number])] # only the keep the non virtual terms
return attribution_dataDeepLIFT for all drugs
attribution_data_all = pd.DataFrame()
# Obtain the top GO terms on all layers for each drug
for selected_drug_data in drugs:
selected_drug =selected_drug_data[0] # DRUG smile
selected_drug_features = []
drug_specific_features=drug_features[drug2id_mapping[selected_drug]] # features of drug
for i in range(len(cell2id_mapping)): # make all combinations of selected_drug and cell types
selected_drug_features.append(np.concatenate((cell_features[i], drug_specific_features), axis=None))
selected_drug_features = torch.FloatTensor(np.array(selected_drug_features))
# Data for deeplift...
input_data = torch.autograd.Variable(selected_drug_features.cuda(0))
#median_drug_features = drug_specific_features
# baseline is the median of the expression data and drug features
baseline = torch.FloatTensor(np.concatenate((median_cell_features, median_drug_features), axis=None))
baseline = torch.reshape(baseline, (1, baseline.size()[0]))
baseline = torch.autograd.Variable(baseline.cuda(0))
attribution_data_drug = list(map(get_layer_attribution,range(0,len(model_layers)),itertools.repeat(input_data, len(model_layers)),itertools.repeat(baseline, len(model_layers)),itertools.repeat(selected_drug_data, len(model_layers)))) # get the attribution for each layer (map is similar to apply)
attribution_data_drug = pd.concat(attribution_data_drug) # concatenate attribution of all layers
attribution_data_all = pd.concat([attribution_data_all,attribution_data_drug.iloc[:,1]], axis=1)
print(selected_drug_data[1])
attribution_data_all = pd.concat([attribution_data_drug.iloc[:,0],attribution_data_all], axis=1)attribution_data_all = attribution_data_all.set_index("GO_term")attribution_data_all.head()| BRD-K02251932-001-01-3 | BRD-K25737009-001-01-2 | Nintedanib | bicalutamide | N-[(2R,3S)-2-[[cyclopropylmethyl(methyl)amino]methyl]-5-[(2R)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-3,4-dihydro-2H-1,5-benzoxazocin-8-yl]-1-methyl-4-imidazolesulfonamide | PHA-665752 | N-cyclopropyl-3-[3-[[cyclopropyl(oxo)methyl]amino]-1H-indazol-6-yl]benzamide | Ki8751 | IPA-3 | FAWUGYGEBHAQBU-PPEXNQRJSA-N | ... | ML031 | Semagacestat | RITA | CDK9 inhibitor | Dasatinib | BMS-536924;CC1=CC(=CC2=C1NC(=C3C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)N2)N5CCOCC5 | SCHEMBL13741284 | Daporinad | STF-31 | Narciclasine | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GO_term | |||||||||||||||||||||
| GO:0000012_1 | -0.006564 | -0.005680 | 0.003188 | -0.005863 | -0.003410 | -0.002951 | 0.001118 | 0.002033 | 0.000799 | -0.007842 | ... | -0.007256 | -0.004271 | -0.013783 | -0.006253 | 0.002864 | 0.009604 | -0.008099 | -0.001475 | -0.003698 | -0.009866 |
| GO:0000012_2 | 0.010029 | 0.011514 | 0.009892 | 0.012072 | 0.005788 | 0.012909 | 0.002316 | 0.009362 | -0.011816 | 0.000166 | ... | 0.008918 | -0.002449 | 0.017704 | 0.006732 | 0.002447 | 0.006485 | 0.003888 | -0.000569 | 0.001628 | 0.017132 |
| GO:0000012_3 | 0.008466 | 0.006840 | -0.000027 | 0.006379 | 0.003082 | -0.006110 | -0.008877 | -0.000347 | -0.013084 | 0.000150 | ... | -0.006096 | 0.011308 | 0.012216 | 0.000997 | 0.011521 | 0.013800 | 0.002843 | 0.016328 | 0.021640 | 0.003536 |
| GO:0000012_4 | 0.013018 | 0.007276 | 0.010128 | 0.008622 | 0.004795 | 0.006706 | 0.000874 | 0.005514 | -0.003347 | -0.000010 | ... | -0.003682 | 0.006544 | 0.010806 | 0.003346 | 0.017556 | 0.023130 | 0.001105 | 0.009710 | 0.016940 | 0.014787 |
| GO:0000012_5 | -0.007076 | -0.006129 | -0.007634 | -0.003785 | -0.004151 | -0.007947 | -0.008430 | -0.006039 | -0.002722 | 0.002163 | ... | 0.001821 | -0.002346 | -0.007831 | -0.009368 | -0.011118 | -0.003408 | -0.001760 | 0.003593 | -0.000109 | -0.020831 |
5 rows × 684 columns
ChEMBL Drug Target Slim
from chembl_webresource_client.new_client import new_clientImport SparseGO drugs
# Get names
def get_compound_names(file_name):
compounds = []
with open(file_name, 'r') as fi:
for line in fi:
tokens = line.strip().split('\t')
compounds.append(tokens[2].lower())
return compounds
names = get_compound_names(computer+"SparseGO_code/data/compound_names.txt")
names.pop(0)'name'
chEML IDs
Get chembl IDs of drugs if available (there are always 684 drugs, the compounds2ids object can be reused)
# Get all chembl IDs -- tarda
molecule = new_client.molecule
compounds2ids = {}
for i,drug in enumerate(names):
if " + " in drug:
drug_split = drug.split(" + ", 1)
ID1 = list(molecule.filter(pref_name__iexact=drug_split[0]).only('molecule_chembl_id'))
ID2 = list(molecule.filter(pref_name__iexact=drug_split[1]).only('molecule_chembl_id'))
if len(ID1)>0 and len(ID2)>0:
compounds2ids[drug]=[ID1[0]['molecule_chembl_id'],ID2[0]['molecule_chembl_id']]
elif len(ID1)>0:
compounds2ids[drug]=ID1[0]['molecule_chembl_id']
elif len(ID2)>0:
compounds2ids[drug]=ID2[0]['molecule_chembl_id']
else:
print(drug,i)
else:
ID = list(molecule.filter(pref_name__iexact=drug).only('molecule_chembl_id'))
if len(ID)>0:
ID = ID[0]['molecule_chembl_id']
compounds2ids[drug]=ID
else:
# for drugs that have the chembl ID as the name!!
ID = list(molecule.filter(chembl_id=drug).only('molecule_chembl_id'))
if len(ID)>0:
ID = ID[0]['molecule_chembl_id']
compounds2ids[drug]=ID
else:
# in case it is not found by pref_name
ID = list(molecule.filter(molecule_synonyms__molecule_synonym__iexact=drug).only('molecule_chembl_id'))
if len(ID)>0:
ID = ID[0]['molecule_chembl_id']
compounds2ids[drug]=ID
else:
print(drug,i)
# 341 chembl IDs where found (october 31 2022)#manually add 6 more
compounds2ids["teniposide [usan]"]="CHEMBL452231"
compounds2ids["docetaxel (taxotere)"]="CHEMBL92"
compounds2ids["nan + navitoclax(1)"]="CHEMBL443684"
compounds2ids["nan + navitoclax(2)"]="CHEMBL443684"
compounds2ids["osi-027;coc1=cc=cc2=cc(=c3c4=c(n=cnn4c(=n3)c5ccc(cc5)c(=o)o)n)n=c21"]="CHEMBL3120215"
compounds2ids["paclitaxel;cc1=c2[c@h](c(=o)[c@@]3([c@h](c[c@@h]4[c@]([c@h]3[c@@h]([c@@](c2(c)c)(c[c@@h]1oc(=o)[c@@h]([c@h](c5=cc=cc=c5)nc(=o)c6=cc=cc=c6)o)o)oc(=o)c7=cc=cc=c7)(co4)oc(=o)c)o)c)oc(=o)c"]="CHEMBL428647"len(compounds2ids)347
chEMBL MoA (targets)
Get the molecule targets of each drug (if available)
compounds2targets = dict() # required to store the drug targets
for drug in compounds2ids.keys():
compounds2targets[drug] = set()
chembl_ids = list(compounds2ids.values()) # Chembl IDs of drugs
for drug in compounds2ids:
# we jump from compounds to targets through activities:
activities = new_client.mechanism.filter(parent_molecule_chembl_id__in=compounds2ids[drug]).only(
['parent_molecule_chembl_id', 'target_chembl_id'])
# extracting target ChEMBL IDs from activities:
for act in activities:
compounds2targets[drug].add(act['target_chembl_id'])
print(drug)
# We now know all targets for some drugcompounds2targets = {k: v for k, v in compounds2targets.items() if len(v) != 0 and len([x for x in list(v) if x is not None]) != 0 }
# 218 DRUGS HAVE ANNOTATED DRUG TARGETSlen(compounds2targets)220
Drug slim GO terms
Get the GO terms of each target
# Get the GO terms of each target
compounds_GOterms = {}
for i in range(0, len(compounds2targets.keys())):
compound = list(compounds2targets.keys())[i]
GOterms_list = []
for j in range(0, len(list(compounds2targets[compound]))):
target = list(compounds2targets[compound])[j]
all_cross_references = list(new_client.target.filter(target_chembl_id=target).only(['target_components']).only(['target_components_xrefs']))[0]['target_components']
if len(all_cross_references)>0: # not all targets have annotated go_terms
for i in range(0, len(all_cross_references)):
GOterms = all_cross_references[i]
GOterms = pd.DataFrame(GOterms['target_component_xrefs'])
GOterms = pd.concat([GOterms,pd.Series([target]).repeat(len(GOterms)).reset_index().pop(0)],axis=1) # add target ID to dataframe
GOterms_list= GOterms_list + GOterms.values.tolist()
compounds_GOterms[compound] = pd.DataFrame(GOterms_list).drop_duplicates()
print(compound)len(compounds_GOterms)220
# we have 206 annotated drugs on CHEMBL# add GO terms found in CTRPv2CTRPv2_terms = pd.read_excel('ctrp_goterms_drugs.xlsx') # add GO terms of drugs with or without annotations
for drug in CTRPv2_terms["Drug"].unique():
if drug not in list(compounds_GOterms.keys()): # some drugs had no previous data, no annotations from chembl
compounds_GOterms[drug] = pd.DataFrame() # create empty dataframe
for term in list(CTRPv2_terms.loc[CTRPv2_terms["Drug"]==drug]["Field"]):
compounds_GOterms[drug] = pd.concat([compounds_GOterms[drug],pd.DataFrame([term,"","GoProcess",""]).transpose()])
compounds_GOterms[drug] = compounds_GOterms[drug].drop_duplicates()
# now we have 233 annotated drugs# Delete drugs with no GOterms (some targets have no annotated GO terms)
compounds_GOterms = {k: v for k, v in compounds_GOterms.items() if len(v) != 0 } len(compounds_GOterms)236
Match GO terms
Find all terms that match, terms that are part of both, the sparseGO graph and the drug slim results…
def load_ontology_extra_output(ontology_file, gene2id_mapping):
"""
Creates the directed graph of the GO terms and stores the connected elements in arrays.
Output
------
dG: networkx.classes.digraph.DiGraph
Directed graph of all terms
terms_pairs: numpy.ndarray
Store the connection between a term and a term
genes_terms_pairs: numpy.ndarray
Store the connection between a gene and a term
"""
dG = nx.DiGraph() # Directed graph class
file_handle = open(ontology_file) # Open the file that has genes and go terms
terms_pairs = [] # store the pairs between a term and a term
genes_terms_pairs = [] # store the pairs between a gene and a term
gene_set = set() # create a set (elements can't repeat)
term_direct_gene_map = {}
term_size_map = {}
for line in file_handle:
line = line.rstrip().split() # delete spaces and transform to list, line has 3 elements
# No me hace falta el if, no tengo que separar las parejas
if line[2] == 'default': # si el tercer elemento es default entonces se conectan los terms en el grafo
dG.add_edge(line[0], line[1]) # Add an edge between line[0] and line[1]
terms_pairs.append([line[0], line[1]]) # Add the pair to the list
else:
if line[1] not in gene2id_mapping: # se salta el gen si no es parte de los que estan en gene2id_mapping
print(line[1])
continue
genes_terms_pairs.append([line[0], line[1]]) # add the pair
if line[0] not in term_direct_gene_map: # si el termino todavia no esta en el diccionario lo agrega
term_direct_gene_map[ line[0] ] = set() # crea un set
term_direct_gene_map[line[0]].add(gene2id_mapping[line[1]]) # añadimos el gen al set de ese term
gene_set.add(line[1]) # añadimos el gen al set total de genes
terms_pairs = np.array(terms_pairs) # convert to 2d array
genes_terms_pairs = np.array(genes_terms_pairs) # convert to 2d array
file_handle.close()
print('There are', len(gene_set), 'genes')
for term in dG.nodes(): # hacemos esto para cada uno de los GO terms
term_gene_set = set() # se crea un set
if term in term_direct_gene_map:
term_gene_set = term_direct_gene_map[term] # genes conectados al term
deslist = nxadag.descendants(dG, term) #regresa todos sus GO terms descendientes (biological processes tiene 2085 descendientes, todos menos el mismo)
for child in deslist:
if child in term_direct_gene_map: # añadir los genes de sus descendientes
term_gene_set = term_gene_set | term_direct_gene_map[child] # union of both sets, ahora tiene todos los genes los suyos y los de sus descendientes
if len(term_gene_set) == 0:
print('There is empty terms, please delete term:', term)
sys.exit(1)
else:
# por ahora esta variable no me hace falta
term_size_map[term] = len(term_gene_set) # cantidad de genes en ese term (tomando en cuenta sus descendientes)
leaves = [n for n in dG.nodes if dG.in_degree(n) == 0] # buscar la raiz
#leaves = [n for n,d in dG.in_degree() if d==0]
uG = dG.to_undirected() # Returns an undirected representation of the digraph
connected_subG_list = list(nxacc.connected_components(uG)) #list of all GO terms
# Verify my graph makes sense...
print('There are', len(leaves), 'roots:', leaves[0])
print('There are', len(dG.nodes()), 'terms')
print('There are', len(connected_subG_list), 'connected components')
if len(leaves) > 1:
print('There are more than 1 root of ontology. Please use only one root.')
sys.exit(1)
if len(connected_subG_list) > 1:
print( 'There are more than connected components. Please connect them.')
sys.exit(1)
return dG, terms_pairs, genes_terms_pairs, term_direct_gene_map, term_size_mapSparseGO graph
# Import SparseGO graph (to extract all nodes/terms)...
# Load ontology: create the graph of connected GO terms
dG, terms_pairs, genes_terms_pairs, term_direct_gene_map, term_size_map = load_ontology_extra_output(onto, gene2id_mapping)
####
sparseGO_terms = list(dG.nodes())
sparseGO_terms.remove("GO:0008150")There are 15015 genes
There are 1 roots: GO:0008150
There are 4184 terms
There are 1 connected components
Full GO graph
# Import full graph (to find parents)...
import obonet
#import networkx as nx
url = 'http://purl.obolibrary.org/obo/go/go-basic.obo'
full_graph = obonet.read_obo(url)
full_graph = full_graph.reverse() # change the direction of nodes
[n for n in full_graph.nodes if full_graph.in_degree(n) == 0] # graph contains the 3 roots (BP,MF,CC)['GO:0003674', 'GO:0005575', 'GO:0008150']
Match terms!
Find all terms that match, terms that are part of both, the sparseGO graph and the drug slim results… if the slim terms’ ascendants are a match, they are also added
# Each model has DIFFERENT matches (the graph is different)
compounds_GOterms_matches = {}
for drug in compounds_GOterms.keys():
# choose drug
drug_df = compounds_GOterms[drug]
drug_slim_GOterms = set(drug_df.loc[drug_df[2] == "GoProcess"][0]) # only GO processes
#set(sparseGO_terms) & set(drug_slim_GOterms)
drug_matches = [] # store all directly matched terms and matches with all parents
for term in drug_slim_GOterms: # term ='GO:1902669' # buen ejemplo
if term in sparseGO_terms: # is the term in the sparseGO terms?
drug_matches.append([1,term]) # add to list
#1: same term, 2:not direct match (esto igual despues...the number indicates how direct is the relationship 0:same term, 1: parent, 2: grandpa, 3:...)
# are its ascendants in the sparseGO terms?
parents = [source for source, _ in full_graph.in_edges(term)] # parents of term
relationship = 2
while(len(parents)>0): # check all ascendants
#relationship+=1
parents = [source for source, _ in full_graph.in_edges(parents)] # parents of parents
for parent_term in parents: # add parents that match sparseGO terms
if parent_term in sparseGO_terms:
drug_matches.append([relationship, parent_term])
drug_matches = (pd.DataFrame(drug_matches).drop_duplicates()).values.tolist() # remove duplicates
compounds_GOterms_matches[drug] = drug_matches
print(drug)# delete drugs that have no matches
compounds_GOterms_matches = {i:j for i,j in compounds_GOterms_matches.items() if j != []}len(compounds_GOterms_matches)230
SparseGO terms x drugSlim terms matrix
attribution_data_all.columns = attribution_data_all.columns.str.lower() # in order to match the termattribution_data_all.head()| brd-k02251932-001-01-3 | brd-k25737009-001-01-2 | nintedanib | bicalutamide | n-[(2r,3s)-2-[[cyclopropylmethyl(methyl)amino]methyl]-5-[(2r)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-3,4-dihydro-2h-1,5-benzoxazocin-8-yl]-1-methyl-4-imidazolesulfonamide | pha-665752 | n-cyclopropyl-3-[3-[[cyclopropyl(oxo)methyl]amino]-1h-indazol-6-yl]benzamide | ki8751 | ipa-3 | fawugygebhaqbu-ppexnqrjsa-n | ... | ml031 | semagacestat | rita | cdk9 inhibitor | dasatinib | bms-536924;cc1=cc(=cc2=c1nc(=c3c(=cc=nc3=o)nc[c@h](c4=cc(=cc=c4)cl)o)n2)n5ccocc5 | schembl13741284 | daporinad | stf-31 | narciclasine | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GO_term | |||||||||||||||||||||
| GO:0000012_1 | -0.006564 | -0.005680 | 0.003188 | -0.005863 | -0.003410 | -0.002951 | 0.001118 | 0.002033 | 0.000799 | -0.007842 | ... | -0.007256 | -0.004271 | -0.013783 | -0.006253 | 0.002864 | 0.009604 | -0.008099 | -0.001475 | -0.003698 | -0.009866 |
| GO:0000012_2 | 0.010029 | 0.011514 | 0.009892 | 0.012072 | 0.005788 | 0.012909 | 0.002316 | 0.009362 | -0.011816 | 0.000166 | ... | 0.008918 | -0.002449 | 0.017704 | 0.006732 | 0.002447 | 0.006485 | 0.003888 | -0.000569 | 0.001628 | 0.017132 |
| GO:0000012_3 | 0.008466 | 0.006840 | -0.000027 | 0.006379 | 0.003082 | -0.006110 | -0.008877 | -0.000347 | -0.013084 | 0.000150 | ... | -0.006096 | 0.011308 | 0.012216 | 0.000997 | 0.011521 | 0.013800 | 0.002843 | 0.016328 | 0.021640 | 0.003536 |
| GO:0000012_4 | 0.013018 | 0.007276 | 0.010128 | 0.008622 | 0.004795 | 0.006706 | 0.000874 | 0.005514 | -0.003347 | -0.000010 | ... | -0.003682 | 0.006544 | 0.010806 | 0.003346 | 0.017556 | 0.023130 | 0.001105 | 0.009710 | 0.016940 | 0.014787 |
| GO:0000012_5 | -0.007076 | -0.006129 | -0.007634 | -0.003785 | -0.004151 | -0.007947 | -0.008430 | -0.006039 | -0.002722 | 0.002163 | ... | 0.001821 | -0.002346 | -0.007831 | -0.009368 | -0.011118 | -0.003408 | -0.001760 | 0.003593 | -0.000109 | -0.020831 |
5 rows × 684 columns
attribution_data_all.shape(25098, 684)
Only keep drugs that have annotated GO terms
attribution_data_annotated = attribution_data_all[list(compounds_GOterms_matches.keys())]attribution_data_annotated.shape # 230 DRUGS(25098, 230)
Build drugSlim (MoA) matrix
slim_matrix = attribution_data_annotated.copy() # copy dataframe in order to build a similar matrix
for col in slim_matrix.columns:
slim_matrix[col].values[:] = 0 # empty matrix for drug in compounds_GOterms_matches.keys():
drug_matches = compounds_GOterms_matches[drug]
drug_matches_names = list(pd.DataFrame(drug_matches)[1])
drug_matches_names_duplicated = []
for term in set(drug_matches_names):
for i in range(1,7):
drug_matches_names_duplicated.append(term+"_"+str(i))
slim_matrix[drug][drug_matches_names_duplicated] = 1 # add a 1 if term is annotated to drugBuild matrices to store logits, predictions and real values
logits_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
preds_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
slim_matrix_single_neuron = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)Create models
Regression models…
# Dictionaries to store results
GO_terms_auc_log = {}
GO_terms_aupr_log = {}
GO_terms_precision_log = {}
# Perform logistic
for goterm in sparseGO_terms:
#store results of each cross validation
# if (real_go_info[real_go_info["GO_term"]==goterm+"_1"]["layer_number"]).values >3:
# continue
all_y_test = []
all_y_pred_proba = []
all_y_pred = []
all_y_names = []
X = []
goterm_drugs = slim_matrix.loc[[goterm+"_"+str(1)]].values.flatten()
if sum(goterm_drugs) <= 8:
continue
list_nodes = []
for i in range(1,7):
list_nodes.append(goterm+"_"+str(i))
score = attribution_data_annotated.loc[list_nodes].T
#score_mod = score
score_mod = score.divide(score.std()).fillna(0) # AFECTA MUCHO
# Separate drugs in 4 groups for cross-validation -----
# Split data in 2 groups (with train_test_split in order to have 0s in both groups)
X_part1,X_part2,y_part1,y_part2=train_test_split(score_mod,goterm_drugs,test_size=0.50,random_state=0,stratify=goterm_drugs)
# Split data again in 4 groups (split data previously split)
X_group1,X_group2,y_group1,y_group2=train_test_split(X_part1,y_part1,test_size=0.50,random_state=0,stratify=y_part1)
X_group3,X_group4,y_group3,y_group4=train_test_split(X_part2,y_part2,test_size=0.50,random_state=0,stratify=y_part2)
for i in range(1,5):
vector = range(0,5)
group_number = str(i)
X_test = globals()["X_group"+group_number]
y_test = globals()["y_group"+group_number]
# Use the other 3 groups for training
keep = list({1,2,3,4}-{int(group_number)}) # remove group number of current test
X_train = pd.concat((globals()["X_group"+str(keep[0])],globals()["X_group"+str(keep[1])],globals()["X_group"+str(keep[2])]))
y_train = np.concatenate((globals()["y_group"+str(keep[0])],globals()["y_group"+str(keep[1])],globals()["y_group"+str(keep[2])]))
logreg = LogisticRegression(penalty="l2",solver="liblinear",max_iter=2000, C=10e-2,class_weight="balanced")
# fit the model with data
logreg.fit(X_train,y_train)
y_pred=logreg.predict(X_test)
y_pred_proba = logreg.predict_proba(X_test)[::,1] # logits for 1 cross-validation
#y_pred_proba = logreg.decision_function(X_test) # signed distance of sample from hyperplane of your model.
all_y_test.append(y_test)
all_y_pred_proba.append(y_pred_proba)
all_y_pred.append(y_pred)
all_y_names.append(X_test.index)
all_y_test = np.concatenate(all_y_test)
all_y_pred_proba = np.concatenate(all_y_pred_proba)
all_y_names = np.concatenate(all_y_names)
all_y_pred = np.concatenate(all_y_pred)
logits_matrix.loc[goterm,all_y_names] = all_y_pred_proba
slim_matrix_single_neuron.loc[goterm,all_y_names] = all_y_test
preds_matrix.loc[goterm,all_y_names] = all_y_pred
# fpr, tpr, _ = metrics.roc_curve(all_y_test, all_y_pred_proba)
# GO_terms_auc_log[goterm] = metrics.auc(fpr, tpr) # same as roc_auc_score
GO_terms_auc_log[goterm] = metrics.roc_auc_score(all_y_test, all_y_pred_proba)
precision, recall, thresholds = metrics.precision_recall_curve(all_y_test, all_y_pred_proba)
GO_terms_aupr_log[goterm] = metrics.auc(recall, precision)
GO_terms_precision_log[goterm] = metrics.precision_score(all_y_test, all_y_pred)NameError: name 'train_test_split' is not defined
class_weight=“balanced”… mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))
C , default=1.0… Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.
solver{‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’}
Los resultados son iguales si uso predict_proba o decision_function, solo a la hora de interpretar predicted_proba si me da un porcentaje y decision_function una distancia a la recta, el AUC me sale exactamente igual en la regresion logistica
GO_terms_auc_log_df = pd.DataFrame(list(GO_terms_auc_log.items()),columns = ['goterm','auc']).set_index("goterm")
GO_terms_auc_log_df = GO_terms_auc_log_df.dropna()
GO_terms_auc_log_df.sort_values(by=["auc"], ascending=False)print("There are " +str(len(GO_terms_auc_log_df))+ " logistic regression models.")# only keep goterms that have a model
logits_matrix = logits_matrix.loc[list(GO_terms_auc_log_df.index),:]
slim_matrix_single_neuron = slim_matrix_single_neuron.loc[list(GO_terms_auc_log_df.index),:]
preds_matrix = preds_matrix.loc[list(GO_terms_auc_log_df.index),:]AUC histogram
sns.set(rc={'figure.figsize':(10,6)})
fig, ax = plt.subplots()
perc = str(round((100*len(GO_terms_auc_log_df[GO_terms_auc_log_df["auc"]>0.69])/len(GO_terms_auc_log_df)),2))+"%"
N, bins, patches = plt.hist(GO_terms_auc_log_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.69:
patches[i].set_facecolor(CB_color_cycle[2])
plt.yticks(fontsize=16)
plt.xticks(fontsize=16)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=True)
# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
plt.xlabel("AUC value", fontsize=20)
plt.ylabel("Number of GO term models", fontsize=20)
colors2 = {'GO term models with AUC>=0.7':CB_color_cycle[2]}
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
plt.legend(handles, labels,fontsize=20, loc="lower left", bbox_to_anchor=(0.35,-0.35))
plt.text(0.71, 8, str(perc), fontsize=20,color='#333333')
plt.title("Overall performance of the models using expression", fontsize=24)
# con el que mejor funciona es con la suma normal del attribution
fig.tight_layout()
fig.savefig(resultsdir+'modelsAUClog.png', transparent=True)AUC boxplot by parents
# Add number of parents
number_parents = {}
levels = {}
for i in range(0,len(GO_terms_auc_log_df.index)):
term = GO_terms_auc_log_df.index[i]
number_parents[GO_terms_auc_log_df.index[i]]=len([source for source, _ in dG.in_edges(term)])
levels[GO_terms_auc_log_df.index[i]]=level_number[term]-1
levels = pd.DataFrame.from_dict(levels, orient='index')
number_parents = pd.DataFrame.from_dict(number_parents, orient='index')
GO_terms_auc_log_df = pd.concat([GO_terms_auc_log_df, levels,number_parents], axis=1)
GO_terms_auc_log_df.columns = ["auc","levels","parents"]sns.set(rc={'figure.figsize':(10,6)})
fig, ax = plt.subplots()
ax = sns.boxplot(x="levels", y="auc", data=GO_terms_auc_log_df)
plt.yticks(fontsize=16)
plt.xticks(fontsize=16)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=True)
# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
plt.xlabel("Level number", fontsize=20)
plt.ylabel("AUC value", fontsize=20)
plt.title("AUC value per level of GO hierarchy", fontsize=24)
# con el que mejor funciona es con la suma normal del attribution
fig.tight_layout()
fig.savefig(resultsdir+'AUCbyLevelslog.png', transparent=True)GO_terms_auc_log_df.head()import pyreadr pyreadr.write_rdata(“C:/Users/ksada/OneDrive - Tecnun/SparseGO_Rdata/cv_allsamples_mutations/”+“GO_terms_auc_logarithm.RData”, GO_terms_auc_log_df.reset_index(), df_name=“GO_terms_auc_logarithm”)
TOP 15 PREDICTED GO TERMS
top15goterms= np.array(GO_terms_auc_log_df.sort_values(by=["auc"], ascending=False)[0:15].index)Get Top GO term names
top15goterms_1 = []
for goterm in top15goterms:
top15goterms_1.append(goterm+"_"+str(1))
real_go_info_mod_best = real_go_info[real_go_info.GO_term.isin(top15goterms_1)]
real_go_info_mod_best.GO_term = real_go_info_mod_best.GO_term.str.replace("_1","")top15goterms_auc = GO_terms_auc_log_df.sort_values(by=["auc"], ascending=False)[0:15].reset_index()
top15goterms_auc.columns=["GO_term","auc","levels","parents"]top15goterms_auc.merge(real_go_info_mod_best[real_go_info_mod_best["GO_term"].isin(top15goterms)], on="GO_term")WORST 15 PREDICTED GO TERMS
worst15goterms= np.array(GO_terms_auc_log_df.sort_values(by=["auc"], ascending=True)[0:15].index)Get Worst GO term names
worst15goterms_1 = []
for goterm in worst15goterms:
worst15goterms_1.append(goterm+"_"+str(1))
real_go_info_mod_worst = real_go_info[real_go_info.GO_term.isin(worst15goterms_1)]
real_go_info_mod_worst.GO_term = real_go_info_mod_worst.GO_term.str.replace("_1","")worst15goterms_auc = GO_terms_auc_log_df.sort_values(by=["auc"], ascending=True)[0:15].reset_index()
worst15goterms_auc.columns=["GO_term","auc","levels","parents"]worst15goterms_auc.merge(real_go_info_mod_worst[real_go_info_mod_worst["GO_term"].isin(worst15goterms)], on="GO_term")AUPR histogram
GO_terms_aupr_log_df = pd.DataFrame(list(GO_terms_aupr_log.items()),columns = ['goterm','aupr']).set_index("goterm")
GO_terms_aupr_log_df = GO_terms_aupr_log_df.dropna()
GO_terms_aupr_log_df.sort_values(by=["aupr"], ascending=False).head()# TENGO PROBLEMA CON EL RECALL
sns.set(rc={'figure.figsize':(5,3)})
perc = str(round((100*len(GO_terms_aupr_log_df[GO_terms_aupr_log_df["aupr"]>0.69])/len(GO_terms_aupr_log_df)),2))+"%"
N, bins, patches = plt.hist(GO_terms_aupr_log_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.69:
patches[i].set_facecolor(CB_color_cycle[3])
plt.xlabel("AUPR", fontsize=16)
plt.title(perc, fontsize=16)Precision histogram
GO_terms_precision_log_df = pd.DataFrame(list(GO_terms_precision_log.items()),columns = ['goterm','precision']).set_index("goterm")
GO_terms_precision_log_df = GO_terms_precision_log_df.dropna()
GO_terms_precision_log_df.sort_values(by=["precision"], ascending=False).head()perc = str(round((100*len(GO_terms_precision_log_df[GO_terms_precision_log_df["precision"]>0.69])/len(GO_terms_precision_log_df)),2))+"%"
N, bins, patches = plt.hist(GO_terms_precision_log_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.69:
patches[i].set_facecolor(CB_color_cycle[4])
plt.xlabel("Precision", fontsize=16)
plt.title(perc, fontsize=16)Example prediction
def f2(goterm):
return gotermcombobox_go = interactive(f2, goterm=widgets.Combobox(options=list(GO_terms_auc_log_df.sort_values(by=["auc"], ascending=False).index)))Choose drug to study…
display(combobox_go)selected_go = combobox_go.result#auc
fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[selected_go], logits_matrix.loc[selected_go])
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[selected_go], logits_matrix.loc[selected_go])
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()
plot = pd.concat([pd.DataFrame(slim_matrix_single_neuron.loc[selected_go]),pd.DataFrame(logits_matrix.loc[selected_go])], axis=1)
plot.columns = ["slim","probability"]
ax = sns.boxplot(x="slim", y="probability", data=plot,showfliers=False )metrics.ConfusionMatrixDisplay.from_predictions(slim_matrix_single_neuron.loc[selected_go], preds_matrix.loc[selected_go])
plt.grid(b=None)
print("Accuracy:",metrics.accuracy_score(slim_matrix_single_neuron.loc[selected_go], preds_matrix.loc[selected_go]))
print("Precision:",metrics.precision_score(slim_matrix_single_neuron.loc[selected_go], preds_matrix.loc[selected_go]))
print("Recall:",metrics.recall_score(slim_matrix_single_neuron.loc[selected_go], preds_matrix.loc[selected_go])) #TP / (TP+FN)TN - FP
FN - TP
precision, recall, thresholds = metrics.precision_recall_curve(slim_matrix_single_neuron.loc[selected_go], logits_matrix.loc[selected_go])
auc_precision_recall = metrics.auc(recall, precision)
plt.plot(recall, precision,label=str(auc_precision_recall))
plt.legend(loc=4)
plt.show()METRICS drugs
auc_drugs = {}
aupr_drugs = {}
for drug in list(slim_matrix_single_neuron.columns):
if slim_matrix_single_neuron.loc[:,drug].sum() ==0:
continue
auc_drugs[drug] = metrics.roc_auc_score(slim_matrix_single_neuron.loc[:,drug], logits_matrix.loc[:,drug])
precision, recall, thresholds = metrics.precision_recall_curve(slim_matrix_single_neuron.loc[:,drug], logits_matrix.loc[:,drug])
aupr_drugs[drug] = metrics.auc(recall, precision)
auc_drugs_df = pd.DataFrame(list(auc_drugs.items()),columns = ['goterm','auc']).set_index("goterm")
auc_drugs_df = auc_drugs_df.dropna()
aupr_drugs_df = pd.DataFrame(list(aupr_drugs.items()),columns = ['goterm','aupr']).set_index("goterm")
aupr_drugs_df = aupr_drugs_df.dropna()AUC histogram drugs
sns.set(rc={'figure.figsize':(10,6)})
fig, ax = plt.subplots()
perc = str(round((100*len(auc_drugs_df[auc_drugs_df["auc"]>0.69])/len(auc_drugs_df)),2))+"%"
N, bins, patches = plt.hist(auc_drugs_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.69:
patches[i].set_facecolor(CB_color_cycle[5])
plt.yticks(fontsize=16)
plt.xticks(fontsize=16)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=True)
# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
plt.xlabel("AUC value", fontsize=20)
plt.ylabel("Number of drugs", fontsize=20)
colors2 = {'Drugs with AUC>=0.7':CB_color_cycle[5]}
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
plt.legend(handles, labels,fontsize=20, loc="lower left", bbox_to_anchor=(0.35,-0.35))
plt.text(0.79, 6, str(perc), fontsize=20,color='#333333')
plt.title("Overall performance by drugs using mutations", fontsize=24)
# con el que mejor funciona es con la suma normal del attribution
fig.tight_layout()
fig.savefig(resultsdir+'drugsAUClog.png', transparent=True)auc_drugs_df.sort_values(by=["auc"], ascending=False)AUPR histogram drugs
sns.set(rc={'figure.figsize':(5,3)})
perc = str(round((100*len(aupr_drugs_df[aupr_drugs_df["aupr"]>0.69])/len(aupr_drugs_df)),2))+"%"
N, bins, patches = plt.hist(aupr_drugs_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.69:
patches[i].set_facecolor(CB_color_cycle[3])
plt.xlabel("AUPR drugs", fontsize=16)
plt.title(perc, fontsize=16)auc_drugs_df.sort_values(by=["auc"], ascending=False)Example drug prediction
def f(drug):
return drugcombobox = interactive(f, drug=widgets.Combobox(options=list(auc_drugs_df.sort_values(by=["auc"], ascending=False).index)))Choose drug to study…
display(combobox)selected_drug_name = combobox.resultsns.set(rc={'figure.figsize':(4,2)})
#auc
fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[:,selected_drug_name], logits_matrix.loc[:,selected_drug_name] )
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[:,selected_drug_name], logits_matrix.loc[:,selected_drug_name])
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()
plot = pd.concat([pd.DataFrame(slim_matrix_single_neuron.loc[:,selected_drug_name]),pd.DataFrame(logits_matrix.loc[:,selected_drug_name])], axis=1)
plot.columns = ["slim","probability"]
ax = sns.boxplot(x="slim", y="probability", data=plot,showfliers=False )sum(slim_matrix_single_neuron.loc[:,selected_drug_name])plot = pd.concat([pd.DataFrame(slim_matrix.loc[:,selected_drug_name]),pd.DataFrame(attribution_data_all.loc[:,selected_drug_name]*1e4)], axis=1)
plot.columns = ["slim","attribution"]
ax = sns.boxplot(x="slim", y="attribution", data=plot,showfliers=True )metrics.ConfusionMatrixDisplay.from_predictions(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_matrix.loc[:,selected_drug_name])
plt.grid(visible=None)
print("Accuracy:",metrics.accuracy_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_matrix.loc[:,selected_drug_name]))
print("Precision:",metrics.precision_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_matrix.loc[:,selected_drug_name]))
print("Recall:",metrics.recall_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_matrix.loc[:,selected_drug_name])) #TP / (TP+FN)
print("AUC with score:",auc) #TP / (TP+FN)View drug’s top functions…
predictions_nodes = []
for goterm in list(logits_matrix.index):
predictions_nodes.append(goterm+"_"+str(1))# add names to go terms
real_go_info_log = real_go_info[real_go_info.GO_term.isin(predictions_nodes)]
real_go_info_log.GO_term = real_go_info_log.GO_term.str.replace("_1","")# LOS LOGITS DE TEST!!
test_drug_logs = pd.DataFrame(logits_matrix.loc[:,selected_drug_name]).reset_index()
test_drug_logs.columns = ["GO_term","probability"]
test_drug_logs = test_drug_logs.merge(real_go_info_log, on="GO_term")
test_drug_logs.sort_values(by=["probability"], ascending=False)sns.set(rc={'figure.figsize':(15,8)})
ax = sns.boxplot(x="layer_number", y="probability", data=test_drug_logs, order=[7,6,5,4,3,2,1,0],showfliers=False)
ax = ax.set(xlabel='General terms - Specific terms')Final model
Once the models have been cross-validated we create the final models using all samples…
GO_terms_auc_log_final = {}
GO_terms_aupr_log_final = {}
GO_terms_precision_log_final = {}
models_log = {}
# Perform logistics
for goterm in sparseGO_terms:
#print(goterm)
goterm_drugs = slim_matrix.loc[[goterm+"_"+str(1)]].values.flatten()
if sum(goterm_drugs) <= 10:
continue
list_nodes = []
for i in range(1,7):
list_nodes.append(goterm+"_"+str(i))
score = attribution_data_annotated.loc[list_nodes].T
score_mod = score.divide(score.std()).fillna(0)
# train and test are the same
X_train = score_mod
X_test = score_mod
y_train = goterm_drugs
y_test = goterm_drugs
logreg = LogisticRegression(penalty="l2",solver="liblinear",max_iter=2000, C=10e-2,class_weight="balanced")
# fit the model with data
logreg.fit(X_train,y_train)
y_pred=logreg.predict(X_test)
#auc
y_pred_proba = logreg.predict_proba(X_test)[::,1]
GO_terms_auc_log_final[goterm] = metrics.roc_auc_score(y_test, y_pred_proba)
precision, recall, thresholds = metrics.precision_recall_curve(y_test, y_pred_proba)
GO_terms_aupr_log_final[goterm] = metrics.auc(recall, precision)
GO_terms_precision_log_final[goterm] = metrics.recall_score(y_test, y_pred)
models_log[goterm]=logreglen(models_log)Final model AUC
GO_terms_auc_log_df_final = pd.DataFrame(list(GO_terms_auc_log_final.items()),columns = ['goterm','auc']).set_index("goterm")
GO_terms_auc_log_df_final = GO_terms_auc_log_df_final.dropna()
GO_terms_auc_log_df_final.sort_values(by=["auc"], ascending=False)sns.set(rc={'figure.figsize':(6,4)})
perc = str(round((100*len(GO_terms_auc_log_df_final[GO_terms_auc_log_df_final["auc"]>0.7])/len(GO_terms_auc_log_df_final)),2))+"%"
N, bins, patches = plt.hist(GO_terms_auc_log_df_final, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.7:
patches[i].set_facecolor(CB_color_cycle[2])
plt.xlabel("AUC (logistic 1)", fontsize=16)
plt.title(perc, fontsize=16)
# con el que mejor funciona es con la suma normal del attribution Final model AUPR
GO_terms_aupr_log_df_final = pd.DataFrame(list(GO_terms_aupr_log_final.items()),columns = ['goterm','aupr']).set_index("goterm")
GO_terms_aupr_log_df_final = GO_terms_aupr_log_df_final.dropna()
GO_terms_aupr_log_df_final.sort_values(by=["aupr"], ascending=False).head()# TENGO PROBLEMA CON EL RECALL
sns.set(rc={'figure.figsize':(5,3)})
perc = str(round((100*len(GO_terms_aupr_log_df_final[GO_terms_aupr_log_df_final["aupr"]>0.7])/len(GO_terms_aupr_log_df_final)),2))+"%"
N, bins, patches = plt.hist(GO_terms_aupr_log_df_final, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.7:
patches[i].set_facecolor(CB_color_cycle[3])
plt.xlabel("AUPR", fontsize=16)
plt.title(perc, fontsize=16)Final model Recall
GO_terms_precision_log_df_final = pd.DataFrame(list(GO_terms_precision_log_final.items()),columns = ['goterm','precision']).set_index("goterm")
GO_terms_precision_log_df_final = GO_terms_precision_log_df_final.dropna()
GO_terms_precision_log_df_final.sort_values(by=["precision"], ascending=False).head()perc = str(round((100*len(GO_terms_precision_log_df_final[GO_terms_precision_log_df_final["precision"]>0.69])/len(GO_terms_precision_log_df_final)),2))+"%"
N, bins, patches = plt.hist(GO_terms_precision_log_df_final, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.69:
patches[i].set_facecolor(CB_color_cycle[4])
plt.xlabel("Recall", fontsize=16)
plt.title(perc, fontsize=16)Predict for a new drug
Make predictions
unknown = list(set(attribution_data_all.columns)-set(attribution_data_annotated.columns))Get the probabilities for all unknown drugs
predictions = {}
probabilities = {}
probabilities_unknown = pd.DataFrame()
for drug in unknown:
for goterm in models_log.keys():
list_nodes = list(models_log[goterm].feature_names_in_) # Extract the feature names from the model (those are the attributions we need)
score = attribution_data_all.loc[list_nodes][drug].to_frame().T
score_mod = score.divide(attribution_data_annotated.loc[list_nodes].T.std()).fillna(0) #divide by std of each neuron, only use drugs that trained the models
#predictions[goterm]=models_log[goterm].predict(score_mod)
#auc
probabilities[goterm] = models_log[goterm].predict_proba(score_mod)[::,1]
drug_probs = pd.DataFrame.from_dict(probabilities).T
drug_probs.columns = [drug]
print(drug)
probabilities_unknown = pd.concat([probabilities_unknown,drug_probs], axis=1)# Save file
with open(resultsdir+'probabilities_unknown_MG2_log_sum.pkl', 'wb') as dictionary_file:
pickle.dump(probabilities_unknown, dictionary_file)IMPORT file
# To import dataframe created before
with open(resultsdir+'probabilities_unknown_MG2_log_sum.pkl', 'rb') as dictionary_file:
probabilities_unknown = pickle.load(dictionary_file) import pyreadr pyreadr.write_rdata(“C:/Users/ksada/OneDrive - Tecnun/SparseGO_Rdata/cv_allsamples_mutations/”+“probabilities_known.RData”, probabilities_unknown.reset_index(), df_name=“probabilities_known”)
Study drug with unknown MOA
Choose drug with unknown MOA…
combobox_u = interactive(f, drug=widgets.Combobox(options=unknown))display(combobox_u)selected_drug_u_name = combobox_u.resultprobabilities_df = pd.DataFrame.from_dict(probabilities_unknown.loc[:,selected_drug_u_name]).reset_index()
probabilities_df.columns = ["GO_term","probability"]
probabilities_df = probabilities_df.merge(real_go_info_log, on="GO_term")
probabilities_df.sort_values(by=["probability"], ascending=False)sns.set(rc={'figure.figsize':(15,8)})
ax = sns.boxplot(x="layer_number", y="probability", data=probabilities_df, order=[7,6,5,4,3,2,1,0],showfliers=False)
ax = ax.set(xlabel='General terms - Specific terms')- bendamustine –> buen ejemplo, parece que tiene sentido lo que sale, es un farmaco para la leucemia y salen cosas de la sangre https://pubchem.ncbi.nlm.nih.gov/compound/65628#section=Chemical-Vendors
- temozolomide –> es para Glioblastoma y me sale brain de los más altos y algo de calcium que tiene algo que ver
- Bleomycin –>
Look for the GO terms (neurons) that vary the most variance between drugs…
# Attribution
topvariance = list(attribution_data_all.var(axis=1).sort_values(axis=0, ascending=False)[0:100].index.values)
data_array = attribution_data_all.loc[topvariance].TOr cluster by the probability…
# Probabilities
topvariance = list(probabilities_unknown.var(axis=1).sort_values(axis=0, ascending=False)[0:200].index.values)
data_array = probabilities_unknown.loc[topvariance].Timport plotly.graph_objects as go
import plotly.figure_factory as ff
from scipy.spatial.distance import pdist, squareform
# get data
labelsGOterms = np.array(data_array.columns)
labelsDrugs = np.array(data_array.index)
# Initialize figure by creating upper dendrogram
fig = ff.create_dendrogram(data_array.T, orientation='bottom',labels=labelsGOterms)
for i in range(len(fig['data'])):
fig['data'][i]['yaxis'] = 'y2'
# Create Side Dendrogram
dendro_side = ff.create_dendrogram(data_array, orientation='right',labels=labelsDrugs)
for i in range(len(dendro_side['data'])):
dendro_side['data'][i]['xaxis'] = 'x2'
# Add Side Dendrogram Data to Figure
for data in dendro_side['data']:
fig.add_trace(data)
# Create Heatmap
heatmap = [
go.Heatmap(
x = fig['layout']['xaxis']['ticktext'],
y = dendro_side['layout']['yaxis']['ticktext'],
z = data_array.loc[dendro_side['layout']['yaxis']['ticktext'],fig['layout']['xaxis']['ticktext']],
zmin=0, zmax=1
)
]
heatmap[0]['x'] = fig['layout']['xaxis']['tickvals']
heatmap[0]['y'] = dendro_side['layout']['yaxis']['tickvals']
# Add Heatmap Data to Figure
for data in heatmap:
fig.add_trace(data)
fig['layout']['yaxis']['ticktext'] = dendro_side['layout']['yaxis']['ticktext']
fig['layout']['yaxis']['tickvals'] = np.asarray(dendro_side['layout']['yaxis']['tickvals'])
# Edit Layout
fig.update_layout({'width':800, 'height':1100,
'showlegend':False, 'hovermode': 'closest',
})
# Edit xaxis
fig.update_layout(xaxis={'domain': [.15, 1],
'mirror': False,
'showgrid': False,
'showline': False,
'zeroline': False,
'ticks':""})
# Edit xaxis2
fig.update_layout(xaxis2={'domain': [0, .15],
'mirror': False,
'showgrid': False,
'showline': False,
'zeroline': False,
'showticklabels': False,
'ticks':""})
# Edit yaxis
fig.update_layout(yaxis={'domain': [0, .85],
'mirror': False,
'showgrid': False,
'showline': False,
'zeroline': False,
'showticklabels': False,
'ticks': ""
})
# Edit yaxis2
fig.update_layout(yaxis2={'domain':[.825, .975],
'mirror': False,
'showgrid': False,
'showline': False,
'zeroline': False,
'showticklabels': False,
'ticks':""})
# Plot!
fig.show()Dendograms - Most commonly created as an output from hierarchical clustering. - The key to interpreting is to focus on the height at which any two objects are joined together. When the height of the link that joins the rows together is the smallest, their are the most similar. - Gives an idea of the number of clusters (but can’t determine the number).
from scipy.stats import ranksums
GO_terms_wilcox = {}
number_ones_w = {}
sum_attribution = {}
#terms_direct_genes = {}
terms_all_genes = {}
# Perform wilcox
for goterm in slim_matrix.index:
goterm_drugs = slim_matrix.loc[[goterm]].T
goterm_drugs.columns = ["slim"]
score = attribution_data_all.loc[[goterm]].T
score.columns = ["score"]
slim_score = goterm_drugs.join(score)
slim_score.columns = ["slim","score"]
number_ones_w[goterm] = sum(goterm_drugs.values.flatten())
sum_attribution[goterm] = sum(score.values.flatten())
#terms_direct_genes[goterm]=len(term_direct_gene_map[goterm[:-2]])
terms_all_genes[goterm]=(term_size_map[goterm[:-2]])
GO_terms_wilcox[goterm] = ranksums(slim_score.loc[slim_score["slim"] == 1]["score"], slim_score.loc[slim_score["slim"] == 0]["score"]).pvalue
#GO_terms_wilcox[goterm] = ranksums(slim_score.loc[slim_score["slim"] == 1]["score"], slim_score.loc[slim_score["slim"] == 0]["score"],alternative="greater").pvalueGO_terms_wilcox_df = pd.DataFrame(list(GO_terms_wilcox.items()),columns = ['goterm','score']).set_index("goterm")
GO_terms_wilcox_df = GO_terms_wilcox_df.dropna()
GO_terms_wilcox_df.sort_values(by=["score"], ascending=True)GO_terms_wilcox_dfPercentage lower than 0.05…
perc = str(round((100*len(GO_terms_wilcox_df[GO_terms_wilcox_df["score"]<0.05])/len(GO_terms_wilcox_df)),2))+"%"sns.set(rc={'figure.figsize':(5,3)})
#sns.histplot(data=GO_terms_wilcox_df, x="score", kde=True, color="olive", bins=100).set(title='Wilcox GO terms - '+perc)
histogram(GO_terms_wilcox_df["score"],CB_color_cycle[0],'Wilcox GO terms - '+perc,"Number of GO terms",n_bins=200)Draw
goterm="GO:1903077_1"
slim = slim_matrix.loc[[goterm]].T
slim.columns = ["slim"]
score = attribution_data_annotated.loc[[goterm]].T
score.columns = ["score"]
score.index = slim.index
plot = slim.join(score)
plot.columns = ["slim","score"]
ax = sns.boxplot(x="slim", y="score", data=plot )# Plotting the KDE Plot
sns.kdeplot(plot.loc[plot["slim"] == 1]["score"], color='orange', shade=True, label=1)
sns.kdeplot(plot.loc[plot["slim"] == 0]["score"], color='blue', shade=True, label=0)
plt.xlabel('Attribution')
plt.ylabel('Probability Density')slim.sum()len(plot)esta no tiene sentido si no es absoluto el valor (porque puede afectar o positiva o negativamente el attribution)
from scipy.stats import ranksums drugs_wilcox = {}
# Perform wilcox
number_parents = {}
levels = {}
for i in range(0,len(slim_matrix.index)):
term = slim_matrix.index[i][:-2]
number_parents[slim_matrix.index[i]]=len([source for source, _ in dG.in_edges(term)])
levels[slim_matrix.index[i]]=level_number[term]-1
levels = pd.DataFrame.from_dict(levels, orient='index')
number_parents = pd.DataFrame.from_dict(number_parents, orient='index')
for drug in slim_matrix.columns:
slim_score = pd.concat([slim_matrix[drug], attribution_data_all[drug],levels], axis=1)
slim_score.columns = ["slim","score","levels"]
#slim_score = slim_score.loc[slim_score["levels"] != 7]
drugs_wilcox[drug] = ranksums(slim_score.loc[slim_score["slim"] == 1]["score"], slim_score.loc[slim_score["slim"] == 0]["score"]).pvalue
#drugs_wilcox[drug] = ranksums(slim_score.loc[slim_score["slim"] == 1]["score"], slim_score.loc[slim_score["slim"] == 0]["score"],alternative="greater").pvaluedrugs_wilcox_df = pd.DataFrame(list(drugs_wilcox.items()),columns = ['goterm','score']).set_index("goterm")
drugs_wilcox_df = drugs_wilcox_df.dropna()
drugs_wilcox_df.sort_values(by=["score"], ascending=True)len(drugs_wilcox_df[drugs_wilcox_df["score"]<0.05])len(drugs_wilcox_df)perc = str(round(100*(len(drugs_wilcox_df[drugs_wilcox_df["score"]<0.05])/len(drugs_wilcox_df)),2))+"%"sns.set(rc={'figure.figsize':(4,4)})
histogram(drugs_wilcox_df["score"],CB_color_cycle[1],'Wilcox Drugs - '+perc,"Number of Drugs",n_bins=10)Draw
drug="selumetinib"plot = pd.concat([slim_matrix[drug], attribution_data_all[drug],number_parents,levels],axis=1)
plot.columns = ["slim","score","parents","levels"]sns.set(rc={'figure.figsize':(4,4)})
ax = sns.boxplot(x="slim", y="score", data=plot,showfliers=True )ranksums(plot.loc[plot["slim"] == 1]["score"], plot.loc[plot["slim"] == 0]["score"]).pvaluesns.set(rc={'figure.figsize':(15,8)})
ax = sns.boxplot(x="levels", y="score", hue="slim", data=plot, order=[7,6,5,4,3,2,1,0])
ax = ax.set(xlabel='General terms - Specific terms')vals = list()
for i in range(0,8):
plot_level = plot.loc[plot["levels"] == i]
pvalue = ranksums(plot_level.loc[plot_level["slim"] == 1]["score"], plot_level.loc[plot_level["slim"] == 0]["score"]).pvalue
vals.append(pvalue)
print("P-value level "+str(i)+": "+str(pvalue))
from scipy.stats import combine_pvalues
cleanedvals = [x for x in vals if ~np.isnan(x)] # delete nans, some levels have only 1 class
combine_pvalues(cleanedvals,method='fisher',weights=None)sns.set(rc={'figure.figsize':(20,8)})
ax = sns.boxplot(x="parents", y="score", hue="slim", data=plot)len(plot.loc[plot["slim"] == 1])/6len(plot.loc[plot["slim"] == 0])/6Wilcox by layers and add by fisher
from scipy.stats import ranksums, combine_pvalues
drugs_wilcox_levels = {}
# Perform wilcox
for drug in slim_matrix.columns:
slim_score = pd.concat([slim_matrix[drug], attribution_data_all[drug],number_parents,levels], axis=1)
slim_score.columns = ["slim","score","parents","levels"]
vals = list()
for i in range(1,27):
slim_score_level = slim_score.loc[slim_score["parents"] == i]
pvalue = ranksums(slim_score_level.loc[slim_score_level["slim"] == 1]["score"], slim_score_level.loc[slim_score_level["slim"] == 0]["score"]).pvalue
vals.append(pvalue)
cleanedvals = [x for x in vals if ~np.isnan(x)] # delete nans, some levels have only 1 class
s, drugs_wilcox_levels[drug] = combine_pvalues(cleanedvals,method='fisher',weights=None)
print(drug)slim_scoredrugs_wilcox_levels_df = pd.DataFrame(list(drugs_wilcox_levels.items()),columns = ['goterm','score']).set_index("goterm")
drugs_wilcox_levels_df = drugs_wilcox_levels_df.dropna()
drugs_wilcox_levels_df.sort_values(by=["score"], ascending=True)sns.set(rc={'figure.figsize':(4,4)})
perc = str(round(100*(len(drugs_wilcox_levels_df[drugs_wilcox_levels_df["score"]<0.05])/len(drugs_wilcox_levels_df)),2))+"%"
histogram(drugs_wilcox_levels_df["score"],CB_color_cycle[1],'Wilcox Drugs - '+perc,"Number of Drugs",n_bins=10)len(slim_matrix.columns)SVM
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.linear_model import LogisticRegressionfrom sklearn import svmslim_matrix_single_neuron = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
preds_svm_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
platt_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
distance_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)
delta_logits_matrix = pd.DataFrame(0, index=sparseGO_terms, columns=slim_matrix.columns)Create models
Regression models…
# Dictionaries to store results
GO_terms_auc_svm = {}
GO_terms_aupr_svm = {}
GO_terms_precision_svm = {}
GO_terms_auc_delta_logits = {}
# Perform logistic
for goterm in sparseGO_terms:
# if (real_go_info[real_go_info["GO_term"]==goterm+"_1"]["layer_number"]).values >3:
# continue
# store results of each cross validation
all_y_test = []
all_y_pred_proba = []
all_y_pred_proba_dis = []
all_y_pred = []
all_y_names = []
goterm_drugs = slim_matrix.loc[[goterm+"_"+str(1)]].values.flatten()
if sum(goterm_drugs) <= 8: # at least 2 annotated drugs in each group
continue
list_nodes = []
for i in range(1,7):
list_nodes.append(goterm+"_"+str(i))
score = attribution_data_annotated.loc[list_nodes].T
#score_mod = score
score_mod = score.divide(score.std()).fillna(0) # AFECTA MUCHO
# Separate drugs in 4 groups for cross-validation -----
# Split data in 2 groups (with train_test_split in order to have 0s in both groups)
X_part1,X_part2,y_part1,y_part2=train_test_split(score_mod,goterm_drugs,test_size=0.50,random_state=0,stratify=goterm_drugs)
# Split data again in 4 groups (split data previously split)
X_group1,X_group2,y_group1,y_group2=train_test_split(X_part1,y_part1,test_size=0.50,random_state=0,stratify=y_part1)
X_group3,X_group4,y_group3,y_group4=train_test_split(X_part2,y_part2,test_size=0.50,random_state=0,stratify=y_part2)
for i in range(1,5):
vector = range(0,5)
group_number = str(i)
X_test = globals()["X_group"+group_number]
y_test = globals()["y_group"+group_number]
# Use the other 3 groups for training
keep = list({1,2,3,4}-{int(group_number)}) # remove group number of current test
X_train = pd.concat((globals()["X_group"+str(keep[0])],globals()["X_group"+str(keep[1])],globals()["X_group"+str(keep[2])]))
y_train = np.concatenate((globals()["y_group"+str(keep[0])],globals()["y_group"+str(keep[1])],globals()["y_group"+str(keep[2])]))
#gamma = 1/(X_train.shape[1]*X_train.to_numpy().var())
gamma = "scale"
C=1
svm_model = svm.SVC(C=C,gamma=gamma, kernel='rbf',
class_weight="balanced",
tol=0.001,
probability=True,
random_state=1234)
# svm_model = svm.SVC(gamma='auto', kernel='rbf',class_weight="balanced",probability=True)
# fit the model with data
svm_model.fit(X_train,y_train)
y_pred=svm_model.predict(X_test)
y_pred_proba = svm_model.predict_proba(X_test)[::,1] # platt values
y_pred_proba_dis = svm_model.decision_function(X_test) # An SVM returns a real-valued prediction for each of the input data samples, which corresponds to its distance from the separating hyperplane.
# decision_function SORTS the results from most probable class to the least probable one.
all_y_test.append(y_test)
all_y_pred_proba.append(y_pred_proba)
all_y_pred_proba_dis.append(y_pred_proba_dis)
all_y_pred.append(y_pred)
all_y_names.append(X_test.index)
all_y_test = np.concatenate(all_y_test)
all_y_pred_proba = np.concatenate(all_y_pred_proba)
all_y_pred_proba_dis = np.concatenate(all_y_pred_proba_dis)
all_y_names = np.concatenate(all_y_names)
all_y_pred = np.concatenate(all_y_pred)
percentage_go_annotations = sum(all_y_test)/len(all_y_test)
logits_apriori=np.log(percentage_go_annotations/(1-percentage_go_annotations))
logits_apost= np.log(all_y_pred_proba/(1-all_y_pred_proba))
delta_logits = logits_apost-logits_apriori
platt_matrix.loc[goterm,all_y_names] = all_y_pred_proba
distance_matrix.loc[goterm,all_y_names] = all_y_pred_proba_dis
slim_matrix_single_neuron.loc[goterm,all_y_names] = all_y_test
preds_svm_matrix.loc[goterm,all_y_names] = all_y_pred
delta_logits_matrix.loc[goterm,all_y_names] = delta_logits
GO_terms_auc_delta_logits[goterm] = metrics.roc_auc_score(all_y_test, delta_logits)
GO_terms_auc_svm[goterm] = metrics.roc_auc_score(all_y_test, all_y_pred_proba)
precision, recall, thresholds = metrics.precision_recall_curve(all_y_test, all_y_pred_proba)
GO_terms_aupr_svm[goterm] = metrics.auc(recall, precision)
GO_terms_precision_svm[goterm] = metrics.precision_score(all_y_test, all_y_pred)# done with platt values
GO_terms_auc_svm_df = pd.DataFrame(list(GO_terms_auc_svm.items()),columns = ['goterm','auc']).set_index("goterm")
GO_terms_auc_svm_df = GO_terms_auc_svm_df.dropna()
GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False).head()| auc | |
|---|---|
| goterm | |
| GO:0036289 | 0.999708 |
| GO:0060440 | 0.994743 |
| GO:0042149 | 0.971292 |
| GO:1902455 | 0.969545 |
| GO:0001556 | 0.965979 |
print("There are " +str(len(GO_terms_auc_svm_df))+ " svm models.")There are 939 svm models.
# only keep goterms that have a model
platt_matrix = platt_matrix.loc[list(GO_terms_auc_svm_df.index),:]
distance_matrix = distance_matrix.loc[list(GO_terms_auc_svm_df.index),:]
slim_matrix_single_neuron = slim_matrix_single_neuron.loc[list(GO_terms_auc_svm_df.index),:]
preds_svm_matrix = preds_svm_matrix.loc[list(GO_terms_auc_svm_df.index),:]
delta_logits_matrix = delta_logits_matrix.loc[list(GO_terms_auc_svm_df.index),:]AUC histogram
sns.set(rc={'figure.figsize':(10,6)})
fig, ax = plt.subplots()
perc = str(round((100*len(GO_terms_auc_svm_df[GO_terms_auc_svm_df["auc"]>0.69])/len(GO_terms_auc_svm_df)),2))+"%"
N, bins, patches = plt.hist(GO_terms_auc_svm_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.69:
patches[i].set_facecolor(CB_color_cycle[2])
plt.yticks(fontsize=16)
plt.xticks(fontsize=16)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=True)
# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
plt.xlabel("AUC value", fontsize=20)
plt.ylabel("Number of GO term models", fontsize=20)
colors2 = {'GO term models with AUC>=0.7':CB_color_cycle[2]}
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
plt.legend(handles, labels,fontsize=20, loc="lower left", bbox_to_anchor=(0.35,-0.35))
plt.text(0.71, 8, str(perc), fontsize=20,color='#333333')
plt.title("Overall performance of the models using expression", fontsize=24)
# con el que mejor funciona es con la suma normal del attribution
fig.tight_layout()
fig.savefig(resultsdir+'modelsAUCsvm.png', transparent=True)
AUC waterfall plot
GO_terms_auc_svm_df =GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False)plt.rcParams['figure.figsize'] = (12, 9)
drugs = GO_terms_auc_svm_df.index
rhos = GO_terms_auc_svm_df["auc"]
percentage = round((sum(rhos>0.69)/len(rhos))*100,1)
fig, ax = plt.subplots()
#colors = ['#208EA3' if (x < 0.5) else '#A4C61A' for x in rhos ]
colors = ['#C9C9C9' if (x < 0.69) else "#6492CA" for x in rhos ]
ax.bar(
x=drugs,
height=rhos,
edgecolor=colors,
linewidth=2
)
plt.xticks([])
plt.yticks(fontsize=28)
# First, let's remove the top, right and left spines (figure borders)
# which really aren't necessary for a bar chart.
# Also, make the bottom spine gray instead of black.
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#ax.spines['bottom'].set_color('#DDDDDD')
# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=False)
# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(False)
ax.yaxis.grid(False)
#ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
# Add labels and a title. Note the use of `labelpad` and `pad` to add some
# extra space between the text and the tick labels.
ax.set_xlabel('SVM models', labelpad=-30, color='#333333',fontsize=50)
ax.set_ylabel('AUC-ROC value', labelpad=15, color='#333333',fontsize=50)
ax.set_title('', color='#333333',
weight='bold')
colors2 = {'High confidence drugs (r>0.5)':'#A4C61A'}
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
#plt.legend(handles, labels,fontsize=40, loc="lower left",bbox_to_anchor=(0, -0.215))
plt.text(77, 0.32, str(percentage)+"%", fontsize=60,color='#000000')
plt.ylim((-0.1,1.1))
# Make the chart fill out the figure better.
fig.tight_layout()
fig.savefig(resultsdir+'WaterfallModelsSVM.png', transparent=True)
AUC boxplot by parents
# Add number of parents
number_parents = {}
levels = {}
for i in range(0,len(GO_terms_auc_svm_df.index)):
term = GO_terms_auc_svm_df.index[i]
number_parents[GO_terms_auc_svm_df.index[i]]=len([source for source, _ in dG.in_edges(term)])
levels[GO_terms_auc_svm_df.index[i]]=level_number[term]-1
levels = pd.DataFrame.from_dict(levels, orient='index')
number_parents = pd.DataFrame.from_dict(number_parents, orient='index')
GO_terms_auc_svm_df = pd.concat([GO_terms_auc_svm_df, levels,number_parents], axis=1)
GO_terms_auc_svm_df.columns = ["auc","levels","parents"]GO_terms_auc_svm_df.head()| auc | levels | parents | |
|---|---|---|---|
| GO:0000077 | 0.284021 | 1 | 3 |
| GO:0045737 | 0.835954 | 0 | 8 |
| GO:0000082 | 0.732331 | 2 | 4 |
| GO:1900087 | 0.593301 | 0 | 10 |
| GO:2000134 | 0.865329 | 1 | 9 |
import plotly.express as px
c = ['#E8384F', '#FD817D', '#FDAE33',
'#EECC16', '#A4C61A', '#37A862',"#208EA3","#3B6EAB"]
df = px.data.tips()
fig = px.box(GO_terms_auc_svm_df, x="levels", y="auc",
color="levels",
color_discrete_sequence=c,
width =600,
height=400,
template="simple_white",
labels=dict(levels="Level of GO hierarchy", auc="AUC-ROC")
)
fig.update_traces(width=0.9)
fig.add_shape( # add a horizontal "target" line
type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0=0, x1=1, xref="paper", y0=0.7, y1=0.7, yref="y"
)
fig.update_layout(
title=dict(text="<b> AUC value grouped by level of GO hierarchy <b>",
x=0.5,
y=0.9,
font=dict(size=18),
xanchor='center',
yanchor='top'),
xaxis=dict(ticks="", showticklabels=False, showgrid=False, zeroline=False),
yaxis=dict(ticks="", showticklabels=True, showgrid=True, zeroline=False),
# yaxis_range=[min(yy.flatten()),max(yy.flatten())],
# xaxis_range=[min(xx.flatten()),max(xx.flatten())],
legend=dict(x=1.1, y=1, orientation="v",font=dict(size=11)),
paper_bgcolor='rgba(0,0,0,0)',
font=dict(family='Roboto',color= "#36382E",size=15)
)
fig.show()TOP 15 PREDICTED GO TERMS
top15goterms= np.array(GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False)[0:15].index)Get Top GO term names
top15goterms_1 = []
for goterm in top15goterms:
top15goterms_1.append(goterm+"_"+str(1))
real_go_info_mod_best = real_go_info[real_go_info.GO_term.isin(top15goterms_1)]
real_go_info_mod_best.GO_term = real_go_info_mod_best.GO_term.str.replace("_1","")top15goterms_auc = GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False)[0:15].reset_index()
top15goterms_auc.columns=["GO_term","auc","levels","parents"]top15goterms_auc = top15goterms_auc.merge(real_go_info_mod_best[real_go_info_mod_best["GO_term"].isin(top15goterms)], on="GO_term")top15goterms_auc| GO_term | auc | levels | parents | Name | layer_number | |
|---|---|---|---|---|---|---|
| 0 | GO:0036289 | 0.999708 | 0 | 2 | Peptidyl-serine autophosphorylation (1) | 0.0 |
| 1 | GO:0060440 | 0.994743 | 0 | 4 | Trachea formation (1) | 0.0 |
| 2 | GO:0042149 | 0.971292 | 0 | 1 | Cellular response to glucose starvation (1) | 0.0 |
| 3 | GO:1902455 | 0.969545 | 0 | 2 | Negative regulation of stem cell population maintenance (1) | 0.0 |
| 4 | GO:0001556 | 0.965979 | 0 | 6 | Oocyte maturation (1) | 0.0 |
| 5 | GO:0045636 | 0.955115 | 0 | 6 | Positive regulation of melanocyte differentiation (1) | 0.0 |
| 6 | GO:0010750 | 0.955000 | 0 | 4 | Positive regulation of nitric oxide mediated signal transduction (1) | 0.0 |
| 7 | GO:0060020 | 0.949434 | 0 | 1 | Bergmann glial cell differentiation (1) | 0.0 |
| 8 | GO:1902042 | 0.945804 | 0 | 4 | Negative regulation of extrinsic apoptotic signaling pathway via death domain receptors (1) | 0.0 |
| 9 | GO:1902236 | 0.941667 | 0 | 12 | Negative regulation of endoplasmic reticulum stress-induced intrinsic apoptotic signaling pathway (1) | 0.0 |
| 10 | GO:0070059 | 0.936432 | 1 | 2 | Intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress (1) | 1.0 |
| 11 | GO:0051453 | 0.935521 | 1 | 2 | Regulation of intracellular ph (1) | 1.0 |
| 12 | GO:0042659 | 0.931364 | 0 | 3 | Regulation of cell fate specification (1) | 0.0 |
| 13 | GO:0006360 | 0.930046 | 2 | 7 | Transcription by rna polymerase i (1) | 2.0 |
| 14 | GO:0006959 | 0.921730 | 2 | 2 | Humoral immune response (1) | 2.0 |
WORST 15 PREDICTED GO TERMS
worst15goterms= np.array(GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=True)[0:15].index)Get Worst GO term names
worst15goterms_1 = []
for goterm in worst15goterms:
worst15goterms_1.append(goterm+"_"+str(1))
real_go_info_mod_worst = real_go_info[real_go_info.GO_term.isin(worst15goterms_1)]
real_go_info_mod_worst.GO_term = real_go_info_mod_worst.GO_term.str.replace("_1","")worst15goterms_auc = GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=True)[0:15].reset_index()
worst15goterms_auc.columns=["GO_term","auc","levels","parents"]worst15goterms_auc.merge(real_go_info_mod_worst[real_go_info_mod_worst["GO_term"].isin(worst15goterms)], on="GO_term")| GO_term | auc | levels | parents | Name | layer_number | |
|---|---|---|---|---|---|---|
| 0 | GO:0000077 | 0.284021 | 1 | 3 | Dna damage checkpoint signaling (1) | 1.0 |
| 1 | GO:0006869 | 0.299648 | 3 | 2 | Lipid transport (1) | 3.0 |
| 2 | GO:0051302 | 0.314545 | 1 | 2 | Regulation of cell division (1) | 1.0 |
| 3 | GO:0016485 | 0.318636 | 3 | 5 | Protein processing (1) | 3.0 |
| 4 | GO:0019722 | 0.322272 | 2 | 1 | Calcium-mediated signaling (1) | 2.0 |
| 5 | GO:0046854 | 0.326276 | 1 | 2 | Phosphatidylinositol phosphate biosynthetic process (1) | 1.0 |
| 6 | GO:0060740 | 0.331825 | 1 | 6 | Prostate gland epithelium morphogenesis (1) | 1.0 |
| 7 | GO:0060444 | 0.347273 | 1 | 8 | Branching involved in mammary gland duct morphogenesis (1) | 1.0 |
| 8 | GO:0006919 | 0.352725 | 1 | 3 | Activation of cysteine-type endopeptidase activity involved in apoptotic process (1) | 1.0 |
| 9 | GO:0032436 | 0.353421 | 1 | 14 | Positive regulation of proteasomal ubiquitin-dependent protein catabolic process (1) | 1.0 |
| 10 | GO:0055119 | 0.353947 | 1 | 1 | Relaxation of cardiac muscle (1) | 1.0 |
| 11 | GO:0001892 | 0.355979 | 1 | 5 | Embryonic placenta development (1) | 1.0 |
| 12 | GO:0031295 | 0.364518 | 0 | 8 | T cell costimulation (1) | 0.0 |
| 13 | GO:0046620 | 0.365476 | 1 | 3 | Regulation of organ growth (1) | 1.0 |
| 14 | GO:0008361 | 0.367423 | 2 | 1 | Regulation of cell size (1) | 2.0 |
AUPR histogram
GO_terms_aupr_svm_df = pd.DataFrame(list(GO_terms_aupr_svm.items()),columns = ['goterm','aupr']).set_index("goterm")
GO_terms_aupr_svm_df = GO_terms_aupr_svm_df.dropna()
GO_terms_aupr_svm_df.sort_values(by=["aupr"], ascending=False).head()| aupr | |
|---|---|
| goterm | |
| GO:0036289 | 0.996209 |
| GO:0006807 | 0.945077 |
| GO:0050896 | 0.921869 |
| GO:0043170 | 0.909722 |
| GO:0009058 | 0.900903 |
# Add number of parents
number_parents = {}
levels = {}
for i in range(0,len(GO_terms_aupr_svm_df.index)):
term = GO_terms_aupr_svm_df.index[i]
number_parents[GO_terms_aupr_svm_df.index[i]]=len([source for source, _ in dG.in_edges(term)])
levels[GO_terms_aupr_svm_df.index[i]]=level_number[term]-1
levels = pd.DataFrame.from_dict(levels, orient='index')
number_parents = pd.DataFrame.from_dict(number_parents, orient='index')
GO_terms_aupr_svm_df = pd.concat([GO_terms_aupr_svm_df, levels,number_parents], axis=1)
GO_terms_aupr_svm_df.columns = ["aupr","levels","parents"]c = ['#E8384F', '#FD817D', '#FDAE33',
'#EECC16', '#A4C61A', '#37A862',"#208EA3","#3B6EAB"]
df = px.data.tips()
fig = px.box(GO_terms_aupr_svm_df, x="levels", y="aupr",
color="levels",
color_discrete_sequence=c,
width =600,
height=400,
template="simple_white",
labels=dict(levels="Level of GO hierarchy", aupr="AUPR")
)
fig.update_traces(width=0.9)
fig.add_shape( # add a horizontal "target" line
type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
x0=0, x1=1, xref="paper", y0=0.7, y1=0.7, yref="y"
)
fig.update_layout(
title=dict(text="<b> AUPR value grouped by level of GO hierarchy <b>",
x=0.5,
y=0.9,
font=dict(size=18),
xanchor='center',
yanchor='top'),
xaxis=dict(ticks="", showticklabels=False, showgrid=False, zeroline=False),
yaxis=dict(ticks="", showticklabels=True, showgrid=True, zeroline=False),
# yaxis_range=[min(yy.flatten()),max(yy.flatten())],
# xaxis_range=[min(xx.flatten()),max(xx.flatten())],
legend=dict(x=1.1, y=1, orientation="v",font=dict(size=11)),
paper_bgcolor='rgba(0,0,0,0)',
font=dict(family='Roboto',color= "#36382E",size=15)
)
fig.show()
pio.write_image(fig, resultsdir+"AUPR_levels.png", width=600, height=400,scale=8)Example prediction
def f2(goterm):
return gotermcombobox_go = interactive(f2, goterm=widgets.Combobox(options=list(GO_terms_auc_svm_df.sort_values(by=["auc"], ascending=False).index)))Choose drug to study…
display(combobox_go)selected_go = combobox_go.result#auc
plt.rcParams['figure.figsize'] = (4, 2)
fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[selected_go], platt_matrix.loc[selected_go])
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[selected_go], platt_matrix.loc[selected_go])
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()
plot = pd.concat([pd.DataFrame(slim_matrix_single_neuron.loc[selected_go]),pd.DataFrame(platt_matrix.loc[selected_go])], axis=1)
plot.columns = ["slim","probability"]
ax = sns.boxplot(x="slim", y="probability", data=plot,showfliers=False )

#auc
fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[selected_go], delta_logits_matrix.loc[selected_go])
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[selected_go], delta_logits_matrix.loc[selected_go])
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()
plot = pd.concat([pd.DataFrame(slim_matrix_single_neuron.loc[selected_go]),pd.DataFrame(delta_logits_matrix.loc[selected_go])], axis=1)
plot.columns = ["slim","probability"]
ax = sns.boxplot(x="slim", y="probability", data=plot,showfliers=False )

plt.rcParams['figure.figsize'] = (2, 2)
metrics.ConfusionMatrixDisplay.from_predictions(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go])
plt.grid(visible=None)
print("Accuracy:",metrics.accuracy_score(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go]))
print("Precision:",metrics.precision_score(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go]))
print("Recall:",metrics.recall_score(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go])) #TP / (TP+FN)
print("AUC with score:",auc) #TP / (TP+FN)Accuracy: 0.9782608695652174
Precision: 0.7894736842105263
Recall: 0.9375
AUC with score: 0.9947429906542057

TN - FP
FN - TP
plt.rcParams['figure.figsize'] = (4, 2)
precision, recall, thresholds = metrics.precision_recall_curve(slim_matrix_single_neuron.loc[selected_go], preds_svm_matrix.loc[selected_go])
auc_precision_recall = metrics.auc(recall, precision)
plt.plot(recall, precision,label=str(auc_precision_recall))
plt.legend(loc=4)
plt.show()
METRICS drugs
auc_drugs = {}
aupr_drugs = {}
precision_drugs = {}
for drug in list(slim_matrix_single_neuron.columns):
if slim_matrix_single_neuron.loc[:,drug].sum() ==0:
continue
#fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[:,drug], logits_matrix.loc[:,drug])
#auc_drugs[drug] = metrics.auc(fpr, tpr)
auc_drugs[drug] = metrics.roc_auc_score(slim_matrix_single_neuron.loc[:,drug], platt_matrix.loc[:,drug])
precision, recall, thresholds = metrics.precision_recall_curve(slim_matrix_single_neuron.loc[:,drug], platt_matrix.loc[:,drug])
aupr_drugs[drug] = metrics.auc(recall, precision)
precision_drugs[drug] = metrics.precision_score(slim_matrix_single_neuron.loc[:,drug], preds_svm_matrix.loc[:,drug])
auc_drugs_df = pd.DataFrame(list(auc_drugs.items()),columns = ['goterm','auc']).set_index("goterm")
auc_drugs_df = auc_drugs_df.dropna()
aupr_drugs_df = pd.DataFrame(list(aupr_drugs.items()),columns = ['goterm','aupr']).set_index("goterm")
aupr_drugs_df = aupr_drugs_df.dropna()
precision_drugs_df = pd.DataFrame(list(precision_drugs.items()),columns = ['goterm','precision']).set_index("goterm")
precision_drugs_df = precision_drugs_df.dropna()AUC histogram drugs
sns.set(rc={'figure.figsize':(10,6)})
fig, ax = plt.subplots()
perc = str(round((100*len(auc_drugs_df[auc_drugs_df["auc"]>0.7])/len(auc_drugs_df)),2))+"%"
N, bins, patches = plt.hist(auc_drugs_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.7:
patches[i].set_facecolor(CB_color_cycle[5])
plt.yticks(fontsize=16)
plt.xticks(fontsize=16)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=True)
# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
plt.xlabel("AUC value", fontsize=20)
plt.ylabel("Number of drugs", fontsize=20)
colors2 = {'Drugs with AUC>=0.7':CB_color_cycle[5]}
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
plt.legend(handles, labels,fontsize=20, loc="lower left", bbox_to_anchor=(0.35,-0.35))
plt.text(0.79, 6, str(perc), fontsize=20,color='#333333')
plt.title("Overall performance by drugs using mutations", fontsize=24)
# con el que mejor funciona es con la suma normal del attribution
fig.tight_layout()
fig.savefig(resultsdir+'drugsAUC.png', transparent=True)
AUC waterfall plot drugs
auc_drugs_df =auc_drugs_df.sort_values(by=["auc"], ascending=False)plt.rcParams['figure.figsize'] = (12, 9)
drugs = auc_drugs_df.index
rhos = auc_drugs_df["auc"]
percentage = round((sum(rhos>0.69)/len(rhos))*100,1)
fig, ax = plt.subplots()
#colors = ['#208EA3' if (x < 0.5) else '#A4C61A' for x in rhos ]
colors = ['#C9C9C9' if (x < 0.69) else "#B678BE" for x in rhos ]
ax.bar(
x=drugs,
height=rhos,
edgecolor=colors,
linewidth=3
)
plt.xticks([])
plt.yticks(fontsize=28)
# First, let's remove the top, right and left spines (figure borders)
# which really aren't necessary for a bar chart.
# Also, make the bottom spine gray instead of black.
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#ax.spines['bottom'].set_color('#DDDDDD')
# Second, remove the ticks as well.
ax.tick_params(bottom=False, left=False)
# Third, add a horizontal grid (but keep the vertical grid hidden).
# Color the lines a light gray as well.
ax.set_axisbelow(False)
ax.yaxis.grid(False)
#ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
# Add labels and a title. Note the use of `labelpad` and `pad` to add some
# extra space between the text and the tick labels.
ax.set_xlabel('Drugs', labelpad=-30, color='#333333',fontsize=50)
ax.set_ylabel('AUC-ROC value', labelpad=15, color='#333333',fontsize=50)
ax.set_title('', color='#333333',
weight='bold')
colors2 = {'High confidence drugs (r>0.5)':'#A4C61A'}
labels = list(colors2.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels]
#plt.legend(handles, labels,fontsize=40, loc="lower left",bbox_to_anchor=(0, -0.215))
plt.text(77, 0.32, str(percentage)+"%", fontsize=60,color='#000000')
plt.ylim((-0.1,1.1))
# Make the chart fill out the figure better.
fig.tight_layout()
fig.savefig(resultsdir+'WaterfallModelsSVM_drugs.png', transparent=True)
AUPR histogram drugs
sns.set(rc={'figure.figsize':(5,3)})
perc = str(round((100*len(aupr_drugs_df[aupr_drugs_df["aupr"]>0.69])/len(aupr_drugs_df)),2))+"%"
N, bins, patches = plt.hist(aupr_drugs_df, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.69:
patches[i].set_facecolor(CB_color_cycle[3])
plt.xlabel("AUPR drugs", fontsize=16)
plt.title(perc, fontsize=16)Text(0.5, 1.0, '33.62%')

Example drug prediction
def f(drug):
return drugpredictions_nodes = []
for goterm in list(platt_matrix.index):
predictions_nodes.append(goterm+"_"+str(1))# add names to go terms
real_go_info_svm= real_go_info[real_go_info.GO_term.isin(predictions_nodes)]
real_go_info_svm.GO_term = real_go_info_svm.GO_term.str.replace("_1","")combobox = interactive(f, drug=widgets.Combobox(options=list(precision_drugs_df.sort_values(by=["precision"], ascending=False).index)))Choose drug to study…
display(combobox)selected_drug_name = combobox.resultsns.set(rc={'figure.figsize':(4,2)})
#auc
fpr, tpr, _ = metrics.roc_curve(slim_matrix_single_neuron.loc[:,selected_drug_name], platt_matrix.loc[:,selected_drug_name] )
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[:,selected_drug_name], platt_matrix.loc[:,selected_drug_name])
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()
plot = pd.concat([pd.DataFrame(slim_matrix_single_neuron.loc[:,selected_drug_name]),pd.DataFrame(platt_matrix.loc[:,selected_drug_name])], axis=1)
plot.columns = ["slim","svm score"]
ax = sns.boxplot(x="slim", y="svm score", data=plot,showfliers=False )

plot = pd.concat([pd.DataFrame(slim_matrix.loc[:,selected_drug_name]),pd.DataFrame(attribution_data_annotated.loc[:,selected_drug_name]*1e4)], axis=1)
plot.columns = ["slim","attribution"]
ax = sns.boxplot(x="slim", y="attribution", data=plot,showfliers=True )
metrics.ConfusionMatrixDisplay.from_predictions(slim_matrix_single_neuron.loc[:,selected_drug_name].round(), preds_svm_matrix.loc[:,selected_drug_name])<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2502863c8b0>

print("Accuracy:",metrics.accuracy_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_svm_matrix.loc[:,selected_drug_name]))
print("Precision:",metrics.precision_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_svm_matrix.loc[:,selected_drug_name]))
print("Recall:",metrics.recall_score(slim_matrix_single_neuron.loc[:,selected_drug_name], preds_svm_matrix.loc[:,selected_drug_name])) #TP / (TP+FN)
print("AUC with score:",auc) Accuracy: 0.663471778487753
Precision: 0.8651026392961877
Recall: 0.5221238938053098
AUC with score: 0.9947429906542057
# LOS LOGITS DE TEST!!
train_drug_logs = pd.DataFrame(delta_logits_matrix.loc[:,selected_drug_name]).reset_index()
train_drug_logs.columns = ["GO_term","probability"]
train_drug_logs = train_drug_logs.merge(real_go_info_svm, on="GO_term")
train_drug_logs.sort_values(by=["probability"], ascending=False)| GO_term | probability | Name | layer_number | |
|---|---|---|---|---|
| 578 | GO:2000379 | 2.770126 | Positive regulation of reactive oxygen species metabolic process (1) | 1.0 |
| 253 | GO:0043552 | 2.707570 | Positive regulation of phosphatidylinositol 3-kinase activity (1) | 0.0 |
| 80 | GO:0010575 | 2.282492 | Positive regulation of vascular endothelial growth factor production (1) | 0.0 |
| 633 | GO:0051301 | 2.245231 | Cell division (1) | 2.0 |
| 224 | GO:0046777 | 2.053782 | Protein autophosphorylation (1) | 1.0 |
| 423 | GO:1902533 | 2.014408 | Positive regulation of intracellular signal transduction (1) | 2.0 |
| 458 | GO:0035025 | 1.952270 | Positive regulation of rho protein signal transduction (1) | 0.0 |
| 848 | GO:0071670 | 1.887644 | Smooth muscle cell chemotaxis (1) | 0.0 |
| 348 | GO:0006939 | 1.842256 | Smooth muscle contraction (1) | 2.0 |
| 98 | GO:0001932 | 1.809520 | Regulation of protein phosphorylation (1) | 4.0 |
| 350 | GO:0045987 | 1.783402 | Positive regulation of smooth muscle contraction (1) | 1.0 |
| 45 | GO:0001501 | 1.682999 | Skeletal system development (1) | 4.0 |
| 908 | GO:0051899 | 1.675960 | Membrane depolarization (1) | 2.0 |
| 653 | GO:0072593 | 1.650121 | Reactive oxygen species metabolic process (1) | 3.0 |
| 115 | GO:0060312 | 1.625762 | Regulation of blood vessel remodeling (1) | 0.0 |
| 926 | GO:0060020 | 1.614859 | Bergmann glial cell differentiation (1) | 0.0 |
| 923 | GO:0048170 | 1.598558 | Positive regulation of long-term neuronal synaptic plasticity (1) | 0.0 |
| 99 | GO:0001934 | 1.593723 | Positive regulation of protein phosphorylation (1) | 3.0 |
| 853 | GO:0038083 | 1.588712 | Peptidyl-tyrosine autophosphorylation (1) | 0.0 |
| 713 | GO:0035726 | 1.577983 | Common myeloid progenitor cell proliferation (1) | 0.0 |
| 445 | GO:0048008 | 1.552362 | Platelet-derived growth factor receptor signaling pathway (1) | 1.0 |
| 857 | GO:0035584 | 1.496390 | Calcium-mediated signaling using intracellular calcium source (1) | 0.0 |
| 333 | GO:1904019 | 1.481165 | Epithelial cell apoptotic process (1) | 1.0 |
| 933 | GO:0051150 | 1.474264 | Regulation of smooth muscle cell differentiation (1) | 1.0 |
| 352 | GO:0014827 | 1.453480 | Intestine smooth muscle contraction (1) | 0.0 |
| 218 | GO:0006468 | 1.436484 | Protein phosphorylation (1) | 5.0 |
| 814 | GO:0090037 | 1.416277 | Positive regulation of protein kinase c signaling (1) | 0.0 |
| 894 | GO:0048017 | 1.399588 | Inositol lipid-mediated signaling (1) | 1.0 |
| 506 | GO:0007286 | 1.376021 | Spermatid development (1) | 1.0 |
| 742 | GO:0035733 | 1.371582 | Hepatic stellate cell activation (1) | 0.0 |
| 10 | GO:0051403 | 1.369506 | Stress-activated mapk cascade (1) | 2.0 |
| 702 | GO:0048146 | 1.349525 | Positive regulation of fibroblast proliferation (1) | 0.0 |
| 782 | GO:1902042 | 1.347889 | Negative regulation of extrinsic apoptotic signaling pathway via death domain receptors (1) | 0.0 |
| 8 | GO:0000165 | 1.328354 | Mapk cascade (1) | 3.0 |
| 531 | GO:0007585 | 1.321161 | Respiratory gaseous exchange by respiratory system (1) | 1.0 |
| 492 | GO:1905065 | 1.288165 | Positive regulation of vascular associated smooth muscle cell differentiation (1) | 0.0 |
| 181 | GO:0006139 | 1.261437 | Nucleobase-containing compound metabolic process (1) | 6.0 |
| 670 | GO:0031274 | 1.250440 | Positive regulation of pseudopodium assembly (1) | 0.0 |
| 424 | GO:0030513 | 1.215934 | Positive regulation of bmp signaling pathway (1) | 0.0 |
| 12 | GO:0070374 | 1.209229 | Positive regulation of erk1 and erk2 cascade (1) | 0.0 |
| 593 | GO:0050865 | 1.191004 | Regulation of cell activation (1) | 5.0 |
| 562 | GO:0050896 | 1.177718 | Response to stimulus (1) | 7.0 |
| 24 | GO:0007346 | 1.156784 | Regulation of mitotic cell cycle (1) | 3.0 |
| 640 | GO:0071310 | 1.131852 | Cellular response to organic substance (1) | 4.0 |
| 159 | GO:0002548 | 1.126395 | Monocyte chemotaxis (1) | 1.0 |
| 142 | GO:0002443 | 1.101635 | Leukocyte mediated immunity (1) | 4.0 |
| 141 | GO:0050900 | 1.098856 | Leukocyte migration (1) | 3.0 |
| 583 | GO:0032967 | 1.096638 | Positive regulation of collagen biosynthetic process (1) | 0.0 |
| 49 | GO:0001569 | 1.083831 | Branching involved in blood vessel morphogenesis (1) | 0.0 |
| 437 | GO:0007169 | 1.082604 | Transmembrane receptor protein tyrosine kinase signaling pathway (1) | 3.0 |
| 723 | GO:0010921 | 1.079534 | Regulation of phosphatase activity (1) | 2.0 |
| 105 | GO:0045860 | 1.071511 | Positive regulation of protein kinase activity (1) | 2.0 |
| 421 | GO:0045747 | 1.070015 | Positive regulation of notch signaling pathway (1) | 0.0 |
| 39 | GO:0008360 | 1.064064 | Regulation of cell shape (1) | 0.0 |
| 444 | GO:0038084 | 1.058233 | Vascular endothelial growth factor signaling pathway (1) | 1.0 |
| 833 | GO:0010467 | 1.038215 | Gene expression (1) | 5.0 |
| 365 | GO:0090141 | 1.033165 | Positive regulation of mitochondrial fission (1) | 0.0 |
| 439 | GO:0030509 | 1.027813 | Bmp signaling pathway (1) | 1.0 |
| 553 | GO:0034394 | 1.017983 | Protein localization to cell surface (1) | 1.0 |
| 60 | GO:0072210 | 1.009552 | Metanephric nephron development (1) | 1.0 |
| 427 | GO:0046427 | 1.007946 | Positive regulation of receptor signaling pathway via jak-stat (1) | 1.0 |
| 684 | GO:0051770 | 1.005599 | Positive regulation of nitric-oxide synthase biosynthetic process (1) | 0.0 |
| 309 | GO:0034765 | 1.001361 | Regulation of ion transmembrane transport (1) | 4.0 |
| 655 | GO:0008210 | 0.998843 | Estrogen metabolic process (1) | 1.0 |
| 486 | GO:0048484 | 0.961996 | Enteric nervous system development (1) | 0.0 |
| 809 | GO:0014068 | 0.961267 | Positive regulation of phosphatidylinositol 3-kinase signaling (1) | 0.0 |
| 441 | GO:0007259 | 0.949624 | Receptor signaling pathway via jak-stat (1) | 2.0 |
| 825 | GO:0036120 | 0.941171 | Cellular response to platelet-derived growth factor stimulus (1) | 0.0 |
| 667 | GO:0035234 | 0.934130 | Ectopic germ cell programmed cell death (1) | 0.0 |
| 23 | GO:0000278 | 0.933767 | Mitotic cell cycle (1) | 4.0 |
| 632 | GO:1900006 | 0.924525 | Positive regulation of dendrite development (1) | 0.0 |
| 397 | GO:0033627 | 0.910092 | Cell adhesion mediated by integrin (1) | 2.0 |
| 764 | GO:0048701 | 0.908694 | Embryonic cranial skeleton morphogenesis (1) | 1.0 |
| 624 | GO:0010628 | 0.896611 | Positive regulation of gene expression (1) | 3.0 |
| 862 | GO:0035162 | 0.872366 | Embryonic hemopoiesis (1) | 1.0 |
| 11 | GO:0043406 | 0.860510 | Positive regulation of map kinase activity (1) | 1.0 |
| 74 | GO:0001817 | 0.857088 | Regulation of cytokine production (1) | 3.0 |
| 743 | GO:0050918 | 0.840184 | Positive chemotaxis (1) | 1.0 |
| 829 | GO:0010212 | 0.825924 | Response to ionizing radiation (1) | 1.0 |
| 922 | GO:0036324 | 0.825686 | Vascular endothelial growth factor receptor-2 signaling pathway (1) | 0.0 |
| 895 | GO:0070528 | 0.820011 | Protein kinase c signaling (1) | 1.0 |
| 664 | GO:0051092 | 0.813750 | Positive regulation of nf-kappab transcription factor activity (1) | 0.0 |
| 27 | GO:0045840 | 0.800332 | Positive regulation of mitotic nuclear division (1) | 1.0 |
| 827 | GO:0010038 | 0.794352 | Response to metal ion (1) | 2.0 |
| 592 | GO:0045595 | 0.785394 | Regulation of cell differentiation (1) | 4.0 |
| 263 | GO:0010559 | 0.783904 | Regulation of glycoprotein biosynthetic process (1) | 1.0 |
| 503 | GO:0021953 | 0.781212 | Central nervous system neuron differentiation (1) | 2.0 |
| 674 | GO:0071276 | 0.780750 | Cellular response to cadmium ion (1) | 0.0 |
| 659 | GO:0048469 | 0.780345 | Cell maturation (1) | 2.0 |
| 652 | GO:0042180 | 0.773541 | Cellular ketone metabolic process (1) | 3.0 |
| 44 | GO:0048812 | 0.771740 | Neuron projection morphogenesis (1) | 3.0 |
| 605 | GO:0051901 | 0.770926 | Positive regulation of mitochondrial depolarization (1) | 0.0 |
| 155 | GO:0030097 | 0.758070 | Hemopoiesis (1) | 4.0 |
| 306 | GO:0043270 | 0.753238 | Positive regulation of ion transport (1) | 3.0 |
| 456 | GO:0046578 | 0.747317 | Regulation of ras protein signal transduction (1) | 2.0 |
| 766 | GO:0051145 | 0.741094 | Smooth muscle cell differentiation (1) | 2.0 |
| 379 | GO:0032956 | 0.736770 | Regulation of actin cytoskeleton organization (1) | 3.0 |
| 898 | GO:0035924 | 0.730231 | Cellular response to vascular endothelial growth factor stimulus (1) | 2.0 |
| 897 | GO:0035767 | 0.729807 | Endothelial cell chemotaxis (1) | 1.0 |
| 817 | GO:0010033 | 0.726109 | Response to organic substance (1) | 5.0 |
| 519 | GO:0048839 | 0.725562 | Inner ear development (1) | 2.0 |
| 378 | GO:0031532 | 0.722138 | Actin cytoskeleton reorganization (1) | 1.0 |
| 106 | GO:0071900 | 0.721523 | Regulation of protein serine/threonine kinase activity (1) | 2.0 |
| 544 | GO:0060179 | 0.712624 | Male mating behavior (1) | 0.0 |
| 763 | GO:0060325 | 0.697674 | Face morphogenesis (1) | 0.0 |
| 277 | GO:0016925 | 0.695177 | Protein sumoylation (1) | 1.0 |
| 328 | GO:0043065 | 0.690749 | Positive regulation of apoptotic process (1) | 2.0 |
| 701 | GO:0002053 | 0.689985 | Positive regulation of mesenchymal cell proliferation (1) | 0.0 |
| 77 | GO:0002718 | 0.688320 | Regulation of cytokine production involved in immune response (1) | 2.0 |
| 691 | GO:0043542 | 0.680928 | Endothelial cell migration (1) | 3.0 |
| 280 | GO:0006810 | 0.677814 | Transport (1) | 7.0 |
| 239 | GO:0006576 | 0.673930 | Cellular biogenic amine metabolic process (1) | 2.0 |
| 858 | GO:0035019 | 0.669360 | Somatic stem cell population maintenance (1) | 1.0 |
| 347 | GO:0006937 | 0.665317 | Regulation of muscle contraction (1) | 2.0 |
| 681 | GO:2001257 | 0.650114 | Regulation of cation channel activity (1) | 2.0 |
| 711 | GO:0019752 | 0.649156 | Carboxylic acid metabolic process (1) | 4.0 |
| 716 | GO:0070662 | 0.644697 | Mast cell proliferation (1) | 0.0 |
| 509 | GO:0060384 | 0.639217 | Innervation (1) | 1.0 |
| 900 | GO:0042060 | 0.629958 | Wound healing (1) | 4.0 |
| 401 | GO:0010811 | 0.627079 | Positive regulation of cell-substrate adhesion (1) | 1.0 |
| 388 | GO:0051726 | 0.624874 | Regulation of cell cycle (1) | 5.0 |
| 144 | GO:0031295 | 0.621763 | T cell costimulation (1) | 0.0 |
| 841 | GO:0032355 | 0.617237 | Response to estradiol (1) | 1.0 |
| 126 | GO:0002318 | 0.612203 | Myeloid progenitor cell differentiation (1) | 0.0 |
| 754 | GO:0009888 | 0.609334 | Tissue development (1) | 4.0 |
| 430 | GO:0019221 | 0.607804 | Cytokine-mediated signaling pathway (1) | 2.0 |
| 97 | GO:0043129 | 0.607469 | Surfactant homeostasis (1) | 0.0 |
| 793 | GO:0009725 | 0.604125 | Response to hormone (1) | 4.0 |
| 147 | GO:0030218 | 0.603194 | Erythrocyte differentiation (1) | 1.0 |
| 675 | GO:0071277 | 0.602376 | Cellular response to calcium ion (1) | 0.0 |
| 806 | GO:0051056 | 0.599956 | Regulation of small gtpase mediated signal transduction (1) | 3.0 |
| 46 | GO:0060348 | 0.599483 | Bone development (1) | 3.0 |
| 837 | GO:0043536 | 0.595296 | Positive regulation of blood vessel endothelial cell migration (1) | 1.0 |
| 863 | GO:0035855 | 0.595221 | Megakaryocyte development (1) | 0.0 |
| 803 | GO:0042475 | 0.592086 | Odontogenesis of dentin-containing tooth (1) | 2.0 |
| 66 | GO:0001824 | 0.588798 | Blastocyst development (1) | 1.0 |
| 495 | GO:0060976 | 0.586013 | Coronary vasculature development (1) | 1.0 |
| 838 | GO:0038033 | 0.585984 | Positive regulation of endothelial cell chemotaxis by vegf-activated vascular endothelial growth factor receptor signaling pathway (1) | 0.0 |
| 626 | GO:0051649 | 0.585010 | Establishment of localization in cell (1) | 4.0 |
| 899 | GO:0035994 | 0.581520 | Response to muscle stretch (1) | 1.0 |
| 398 | GO:0045785 | 0.575910 | Positive regulation of cell adhesion (1) | 3.0 |
| 471 | GO:0060045 | 0.574196 | Positive regulation of cardiac muscle cell proliferation (1) | 0.0 |
| 617 | GO:0008354 | 0.569914 | Germ cell migration (1) | 0.0 |
| 331 | GO:0071887 | 0.563795 | Leukocyte apoptotic process (1) | 2.0 |
| 508 | GO:0042552 | 0.562630 | Myelination (1) | 2.0 |
| 47 | GO:0048704 | 0.560470 | Embryonic skeletal system morphogenesis (1) | 2.0 |
| 386 | GO:0007049 | 0.553817 | Cell cycle (1) | 6.0 |
| 346 | GO:0006936 | 0.550409 | Muscle contraction (1) | 3.0 |
| 225 | GO:0006470 | 0.541583 | Protein dephosphorylation (1) | 3.0 |
| 750 | GO:0044281 | 0.539816 | Small molecule metabolic process (1) | 5.0 |
| 835 | GO:0016239 | 0.538943 | Positive regulation of macroautophagy (1) | 1.0 |
| 219 | GO:0006975 | 0.532966 | Dna damage induced protein phosphorylation (1) | 0.0 |
| 573 | GO:0010629 | 0.530721 | Negative regulation of gene expression (1) | 3.0 |
| 354 | GO:0050728 | 0.528698 | Negative regulation of inflammatory response (1) | 2.0 |
| 746 | GO:0033327 | 0.520751 | Leydig cell differentiation (1) | 0.0 |
| 802 | GO:0060766 | 0.520552 | Negative regulation of androgen receptor signaling pathway (1) | 0.0 |
| 249 | GO:0019216 | 0.518909 | Regulation of lipid metabolic process (1) | 3.0 |
| 231 | GO:0033619 | 0.516921 | Membrane protein proteolysis (1) | 1.0 |
| 428 | GO:0007219 | 0.510598 | Notch signaling pathway (1) | 1.0 |
| 906 | GO:0043549 | 0.506907 | Regulation of kinase activity (1) | 3.0 |
| 59 | GO:0035788 | 0.505476 | Cell migration involved in metanephros development (1) | 0.0 |
| 708 | GO:0050890 | 0.505448 | Cognition (1) | 2.0 |
| 468 | GO:1903010 | 0.502791 | Regulation of bone development (1) | 0.0 |
| 912 | GO:0043534 | 0.496543 | Blood vessel endothelial cell migration (1) | 2.0 |
| 148 | GO:0050852 | 0.496154 | T cell receptor signaling pathway (1) | 1.0 |
| 639 | GO:0060326 | 0.490465 | Cell chemotaxis (1) | 2.0 |
| 317 | GO:0006897 | 0.488535 | Endocytosis (1) | 3.0 |
| 465 | GO:0007275 | 0.485383 | Multicellular organism development (1) | 7.0 |
| 772 | GO:0060485 | 0.484666 | Mesenchyme development (1) | 3.0 |
| 823 | GO:0045471 | 0.483879 | Response to ethanol (1) | 1.0 |
| 373 | GO:0030838 | 0.479914 | Positive regulation of actin filament polymerization (1) | 0.0 |
| 160 | GO:0002573 | 0.478546 | Myeloid leukocyte differentiation (1) | 3.0 |
| 877 | GO:0060437 | 0.473475 | Lung growth (1) | 0.0 |
| 245 | GO:0006629 | 0.469312 | Lipid metabolic process (1) | 5.0 |
| 114 | GO:0051894 | 0.466675 | Positive regulation of focal adhesion assembly (1) | 0.0 |
| 406 | GO:0048041 | 0.466386 | Focal adhesion assembly (1) | 1.0 |
| 932 | GO:0097021 | 0.465441 | Lymphocyte migration into lymphoid organs (1) | 0.0 |
| 339 | GO:2000352 | 0.461676 | Negative regulation of endothelial cell apoptotic process (1) | 0.0 |
| 874 | GO:1903053 | 0.452737 | Regulation of extracellular matrix organization (1) | 1.0 |
| 284 | GO:0051050 | 0.451125 | Positive regulation of transport (1) | 4.0 |
| 13 | GO:0046330 | 0.444601 | Positive regulation of jnk cascade (1) | 0.0 |
| 256 | GO:0046474 | 0.443735 | Glycerophospholipid biosynthetic process (1) | 2.0 |
| 588 | GO:0019222 | 0.443139 | Regulation of metabolic process (1) | 7.0 |
| 459 | GO:0007267 | 0.428833 | Cell-cell signaling (1) | 5.0 |
| 919 | GO:0046677 | 0.427086 | Response to antibiotic (1) | 1.0 |
| 462 | GO:0007268 | 0.426575 | Chemical synaptic transmission (1) | 4.0 |
| 706 | GO:0043547 | 0.426347 | Positive regulation of gtpase activity (1) | 1.0 |
| 660 | GO:0045347 | 0.425159 | Negative regulation of mhc class ii biosynthetic process (1) | 0.0 |
| 290 | GO:2000300 | 0.421019 | Regulation of synaptic vesicle exocytosis (1) | 1.0 |
| 791 | GO:0009582 | 0.420489 | Detection of abiotic stimulus (1) | 2.0 |
| 907 | GO:0051881 | 0.420291 | Regulation of mitochondrial membrane potential (1) | 1.0 |
| 517 | GO:0043586 | 0.419816 | Tongue development (1) | 1.0 |
| 533 | GO:0030168 | 0.419395 | Platelet activation (1) | 2.0 |
| 469 | GO:0060173 | 0.411322 | Limb development (1) | 1.0 |
| 90 | GO:0072284 | 0.410934 | Metanephric s-shaped body morphogenesis (1) | 0.0 |
| 780 | GO:0046890 | 0.404557 | Regulation of lipid biosynthetic process (1) | 2.0 |
| 229 | GO:0016485 | 0.400777 | Protein processing (1) | 3.0 |
| 446 | GO:0048010 | 0.399575 | Vascular endothelial growth factor receptor signaling pathway (1) | 1.0 |
| 402 | GO:0022407 | 0.398607 | Regulation of cell-cell adhesion (1) | 3.0 |
| 287 | GO:0099111 | 0.397633 | Microtubule-based transport (1) | 3.0 |
| 194 | GO:0006355 | 0.390046 | Regulation of transcription, dna-templated (1) | 4.0 |
| 913 | GO:0090630 | 0.382248 | Activation of gtpase activity (1) | 0.0 |
| 122 | GO:0002062 | 0.381242 | Chondrocyte differentiation (1) | 2.0 |
| 629 | GO:0051174 | 0.380683 | Regulation of phosphorus metabolic process (1) | 6.0 |
| 668 | GO:0010976 | 0.374728 | Positive regulation of neuron projection development (1) | 1.0 |
| 604 | GO:0045088 | 0.374664 | Regulation of innate immune response (1) | 2.0 |
| 139 | GO:0002684 | 0.374517 | Positive regulation of immune system process (1) | 4.0 |
| 250 | GO:0044255 | 0.373685 | Cellular lipid metabolic process (1) | 4.0 |
| 163 | GO:0002685 | 0.368480 | Regulation of leukocyte migration (1) | 2.0 |
| 369 | GO:0055003 | 0.366810 | Cardiac myofibril assembly (1) | 0.0 |
| 265 | GO:0051247 | 0.362984 | Positive regulation of protein metabolic process (1) | 4.0 |
| 795 | GO:0043627 | 0.362212 | Response to estrogen (1) | 1.0 |
| 707 | GO:0007202 | 0.359908 | Activation of phospholipase c activity (1) | 0.0 |
| 223 | GO:0018108 | 0.359532 | Peptidyl-tyrosine phosphorylation (1) | 3.0 |
| 832 | GO:0042220 | 0.359499 | Response to cocaine (1) | 1.0 |
| 715 | GO:0061351 | 0.358465 | Neural precursor cell proliferation (1) | 3.0 |
| 873 | GO:0002327 | 0.358081 | Immature b cell differentiation (1) | 0.0 |
| 16 | GO:0046329 | 0.357586 | Negative regulation of jnk cascade (1) | 1.0 |
| 409 | GO:0007166 | 0.352431 | Cell surface receptor signaling pathway (1) | 4.0 |
| 753 | GO:1901135 | 0.352161 | Carbohydrate derivative metabolic process (1) | 4.0 |
| 69 | GO:0001755 | 0.350614 | Neural crest cell migration (1) | 1.0 |
| 440 | GO:0030512 | 0.350318 | Negative regulation of transforming growth factor beta receptor signaling pathway (1) | 0.0 |
| 563 | GO:1900272 | 0.349758 | Negative regulation of long-term synaptic potentiation (1) | 0.0 |
| 650 | GO:0034329 | 0.348915 | Cell junction assembly (1) | 2.0 |
| 230 | GO:0030162 | 0.336130 | Regulation of proteolysis (1) | 3.0 |
| 175 | GO:0005975 | 0.330773 | Carbohydrate metabolic process (1) | 4.0 |
| 627 | GO:0035306 | 0.328011 | Positive regulation of dephosphorylation (1) | 1.0 |
| 400 | GO:0007160 | 0.326200 | Cell-matrix adhesion (1) | 2.0 |
| 455 | GO:0016601 | 0.324410 | Rac protein signal transduction (1) | 2.0 |
| 697 | GO:0050678 | 0.322499 | Regulation of epithelial cell proliferation (1) | 3.0 |
| 71 | GO:0060444 | 0.322093 | Branching involved in mammary gland duct morphogenesis (1) | 1.0 |
| 226 | GO:0035304 | 0.320405 | Regulation of protein dephosphorylation (1) | 2.0 |
| 412 | GO:0009966 | 0.306725 | Regulation of signal transduction (1) | 5.0 |
| 914 | GO:0046486 | 0.298756 | Glycerolipid metabolic process (1) | 3.0 |
| 539 | GO:0050795 | 0.293322 | Regulation of behavior (1) | 2.0 |
| 86 | GO:0072006 | 0.288499 | Nephron development (1) | 2.0 |
| 337 | GO:0043525 | 0.284469 | Positive regulation of neuron apoptotic process (1) | 0.0 |
| 625 | GO:0051902 | 0.284041 | Negative regulation of mitochondrial depolarization (1) | 0.0 |
| 58 | GO:0003338 | 0.282674 | Metanephros morphogenesis (1) | 1.0 |
| 811 | GO:0043124 | 0.281702 | Negative regulation of i-kappab kinase/nf-kappab signaling (1) | 0.0 |
| 812 | GO:0051897 | 0.280267 | Positive regulation of protein kinase b signaling (1) | 0.0 |
| 203 | GO:0006163 | 0.279176 | Purine nucleotide metabolic process (1) | 3.0 |
| 543 | GO:0007612 | 0.277571 | Learning (1) | 1.0 |
| 466 | GO:0007389 | 0.275989 | Pattern specification process (1) | 3.0 |
| 883 | GO:0032148 | 0.273937 | Activation of protein kinase b activity (1) | 0.0 |
| 483 | GO:0035909 | 0.270466 | Aorta morphogenesis (1) | 1.0 |
| 755 | GO:0016358 | 0.270034 | Dendrite development (1) | 3.0 |
| 307 | GO:0034766 | 0.269283 | Negative regulation of ion transmembrane transport (1) | 1.0 |
| 513 | GO:0030900 | 0.265273 | Forebrain development (1) | 3.0 |
| 479 | GO:0048598 | 0.253519 | Embryonic morphogenesis (1) | 4.0 |
| 507 | GO:0007416 | 0.252929 | Synapse assembly (1) | 1.0 |
| 860 | GO:0051928 | 0.247690 | Positive regulation of calcium ion transport (1) | 2.0 |
| 651 | GO:0050808 | 0.247245 | Synapse organization (1) | 3.0 |
| 890 | GO:0071353 | 0.244414 | Cellular response to interleukin-4 (1) | 1.0 |
| 392 | GO:0032467 | 0.243007 | Positive regulation of cytokinesis (1) | 0.0 |
| 695 | GO:0015980 | 0.237863 | Energy derivation by oxidation of organic compounds (1) | 3.0 |
| 235 | GO:0032436 | 0.237590 | Positive regulation of proteasomal ubiquitin-dependent protein catabolic process (1) | 1.0 |
| 560 | GO:0043473 | 0.231280 | Pigmentation (1) | 2.0 |
| 463 | GO:0035249 | 0.230532 | Synaptic transmission, glutamatergic (1) | 1.0 |
| 870 | GO:0070527 | 0.229018 | Platelet aggregation (1) | 1.0 |
| 273 | GO:0016575 | 0.227418 | Histone deacetylation (1) | 2.0 |
| 32 | GO:0000422 | 0.224098 | Autophagy of mitochondrion (1) | 2.0 |
| 113 | GO:0001952 | 0.223395 | Regulation of cell-matrix adhesion (1) | 1.0 |
| 9 | GO:0043408 | 0.223296 | Regulation of mapk cascade (1) | 2.0 |
| 125 | GO:0002244 | 0.223128 | Hematopoietic progenitor cell differentiation (1) | 2.0 |
| 132 | GO:0002274 | 0.221454 | Myeloid leukocyte activation (1) | 2.0 |
| 869 | GO:0030318 | 0.209787 | Melanocyte differentiation (1) | 1.0 |
| 0 | GO:0000077 | 0.209594 | Dna damage checkpoint signaling (1) | 1.0 |
| 255 | GO:0006644 | 0.209513 | Phospholipid metabolic process (1) | 3.0 |
| 645 | GO:0071300 | 0.209465 | Cellular response to retinoic acid (1) | 0.0 |
| 729 | GO:0120035 | 0.207325 | Regulation of plasma membrane bounded cell projection organization (1) | 3.0 |
| 796 | GO:0097067 | 0.207250 | Cellular response to thyroid hormone stimulus (1) | 0.0 |
| 516 | GO:0007423 | 0.206232 | Sensory organ development (1) | 3.0 |
| 410 | GO:0007186 | 0.205939 | G protein-coupled receptor signaling pathway (1) | 3.0 |
| 722 | GO:0042325 | 0.203618 | Regulation of phosphorylation (1) | 5.0 |
| 794 | GO:0043434 | 0.202441 | Response to peptide hormone (1) | 3.0 |
| 88 | GO:0090184 | 0.201141 | Positive regulation of kidney development (1) | 0.0 |
| 431 | GO:0031663 | 0.200051 | Lipopolysaccharide-mediated signaling pathway (1) | 1.0 |
| 866 | GO:0030101 | 0.198593 | Natural killer cell activation (1) | 2.0 |
| 38 | GO:0000902 | 0.193896 | Cell morphogenesis (1) | 4.0 |
| 259 | GO:0009259 | 0.192137 | Ribonucleotide metabolic process (1) | 3.0 |
| 703 | GO:0048661 | 0.191886 | Positive regulation of smooth muscle cell proliferation (1) | 1.0 |
| 740 | GO:0014911 | 0.191054 | Positive regulation of smooth muscle cell migration (1) | 1.0 |
| 714 | GO:0050673 | 0.189629 | Epithelial cell proliferation (1) | 4.0 |
| 43 | GO:0050770 | 0.188075 | Regulation of axonogenesis (1) | 2.0 |
| 381 | GO:0008064 | 0.187642 | Regulation of actin polymerization or depolymerization (1) | 2.0 |
| 851 | GO:0070933 | 0.185342 | Histone h4 deacetylation (1) | 0.0 |
| 762 | GO:0060749 | 0.181899 | Mammary gland alveolus development (1) | 0.0 |
| 454 | GO:0007266 | 0.181338 | Rho protein signal transduction (1) | 1.0 |
| 541 | GO:0008542 | 0.180964 | Visual learning (1) | 0.0 |
| 499 | GO:1990384 | 0.180877 | Hyaloid vascular plexus regression (1) | 0.0 |
| 320 | GO:0006914 | 0.178279 | Autophagy (1) | 4.0 |
| 42 | GO:0048675 | 0.176485 | Axon extension (1) | 2.0 |
| 234 | GO:0010952 | 0.174912 | Positive regulation of peptidase activity (1) | 2.0 |
| 192 | GO:0006352 | 0.168084 | Dna-templated transcription, initiation (1) | 3.0 |
| 448 | GO:0008277 | 0.168080 | Regulation of g protein-coupled receptor signaling pathway (1) | 2.0 |
| 582 | GO:1902459 | 0.162029 | Positive regulation of stem cell population maintenance (1) | 0.0 |
| 489 | GO:0021766 | 0.161871 | Hippocampus development (1) | 1.0 |
| 154 | GO:0050778 | 0.157385 | Positive regulation of immune response (1) | 3.0 |
| 187 | GO:0071897 | 0.156799 | Dna biosynthetic process (1) | 2.0 |
| 7 | GO:0000122 | 0.155105 | Negative regulation of transcription by rna polymerase ii (1) | 1.0 |
| 821 | GO:0034097 | 0.153698 | Response to cytokine (1) | 3.0 |
| 124 | GO:0002218 | 0.151919 | Activation of innate immune response (1) | 1.0 |
| 699 | GO:0033689 | 0.151375 | Negative regulation of osteoblast proliferation (1) | 0.0 |
| 288 | GO:0032940 | 0.151138 | Secretion by cell (1) | 5.0 |
| 854 | GO:0070102 | 0.145028 | Interleukin-6-mediated signaling pathway (1) | 0.0 |
| 361 | GO:0010638 | 0.143294 | Positive regulation of organelle organization (1) | 2.0 |
| 612 | GO:0019827 | 0.141470 | Stem cell population maintenance (1) | 2.0 |
| 781 | GO:0008625 | 0.139268 | Extrinsic apoptotic signaling pathway via death domain receptors (1) | 1.0 |
| 101 | GO:0033141 | 0.136334 | Positive regulation of peptidyl-serine phosphorylation of stat protein (1) | 0.0 |
| 611 | GO:1902904 | 0.136088 | Negative regulation of supramolecular fiber organization (1) | 2.0 |
| 643 | GO:0071222 | 0.135913 | Cellular response to lipopolysaccharide (1) | 2.0 |
| 394 | GO:0007059 | 0.133987 | Chromosome segregation (1) | 3.0 |
| 824 | GO:0071363 | 0.126406 | Cellular response to growth factor stimulus (1) | 3.0 |
| 53 | GO:0001570 | 0.126375 | Vasculogenesis (1) | 1.0 |
| 738 | GO:0098586 | 0.123247 | Cellular response to virus (1) | 1.0 |
| 472 | GO:0001553 | 0.119373 | Luteinization (1) | 0.0 |
| 109 | GO:1905564 | 0.116462 | Positive regulation of vascular endothelial cell proliferation (1) | 0.0 |
| 709 | GO:0019233 | 0.115463 | Sensory perception of pain (1) | 1.0 |
| 728 | GO:0030032 | 0.113460 | Lamellipodium assembly (1) | 1.0 |
| 257 | GO:0046488 | 0.111935 | Phosphatidylinositol metabolic process (1) | 2.0 |
| 313 | GO:0051281 | 0.111192 | Positive regulation of release of sequestered calcium ion into cytosol (1) | 1.0 |
| 924 | GO:0035754 | 0.107144 | B cell chemotaxis (1) | 0.0 |
| 658 | GO:0048468 | 0.103878 | Cell development (1) | 5.0 |
| 545 | GO:0008016 | 0.102922 | Regulation of heart contraction (1) | 3.0 |
| 532 | GO:0007596 | 0.102694 | Blood coagulation (1) | 3.0 |
| 103 | GO:0042531 | 0.102081 | Positive regulation of tyrosine phosphorylation of stat protein (1) | 0.0 |
| 938 | GO:0007158 | 0.100508 | Neuron cell-cell adhesion (1) | 0.0 |
| 383 | GO:0051497 | 0.100321 | Negative regulation of stress fiber assembly (1) | 0.0 |
| 57 | GO:0001656 | 0.100188 | Metanephros development (1) | 2.0 |
| 83 | GO:0002720 | 0.098565 | Positive regulation of cytokine production involved in immune response (1) | 1.0 |
| 382 | GO:0030041 | 0.097715 | Actin filament polymerization (1) | 2.0 |
| 89 | GO:0072239 | 0.093313 | Metanephric glomerulus vasculature development (1) | 0.0 |
| 180 | GO:0019318 | 0.092513 | Hexose metabolic process (1) | 2.0 |
| 555 | GO:1903078 | 0.092013 | Positive regulation of protein localization to plasma membrane (1) | 1.0 |
| 434 | GO:0048011 | 0.091817 | Neurotrophin trk receptor signaling pathway (1) | 1.0 |
| 119 | GO:0060740 | 0.091714 | Prostate gland epithelium morphogenesis (1) | 1.0 |
| 95 | GO:0001894 | 0.091179 | Tissue homeostasis (1) | 2.0 |
| 661 | GO:0046326 | 0.090707 | Positive regulation of glucose import (1) | 0.0 |
| 435 | GO:0016055 | 0.090703 | Wnt signaling pathway (1) | 2.0 |
| 613 | GO:0032922 | 0.089808 | Circadian regulation of gene expression (1) | 0.0 |
| 450 | GO:0051209 | 0.087976 | Release of sequestered calcium ion into cytosol (1) | 3.0 |
| 477 | GO:0040016 | 0.085668 | Embryonic cleavage (1) | 0.0 |
| 607 | GO:0042391 | 0.085377 | Regulation of membrane potential (1) | 3.0 |
| 669 | GO:0010592 | 0.084513 | Positive regulation of lamellipodium assembly (1) | 0.0 |
| 170 | GO:0003007 | 0.084095 | Heart morphogenesis (1) | 3.0 |
| 269 | GO:0031397 | 0.081637 | Negative regulation of protein ubiquitination (1) | 1.0 |
| 206 | GO:0006298 | 0.080826 | Mismatch repair (1) | 0.0 |
| 31 | GO:0070301 | 0.080430 | Cellular response to hydrogen peroxide (1) | 1.0 |
| 130 | GO:0002819 | 0.080102 | Regulation of adaptive immune response (1) | 3.0 |
| 29 | GO:1901031 | 0.077813 | Regulation of response to reactive oxygen species (1) | 1.0 |
| 242 | GO:0090314 | 0.077607 | Positive regulation of protein targeting to membrane (1) | 0.0 |
| 417 | GO:0060395 | 0.076956 | Smad protein signal transduction (1) | 1.0 |
| 710 | GO:0055119 | 0.076428 | Relaxation of cardiac muscle (1) | 1.0 |
| 567 | GO:0051641 | 0.073979 | Cellular localization (1) | 5.0 |
| 491 | GO:0060440 | 0.069181 | Trachea formation (1) | 0.0 |
| 608 | GO:0043114 | 0.069090 | Regulation of vascular permeability (1) | 1.0 |
| 190 | GO:0045739 | 0.068981 | Positive regulation of dna repair (1) | 1.0 |
| 283 | GO:0051049 | 0.068824 | Regulation of transport (1) | 5.0 |
| 595 | GO:0051302 | 0.067812 | Regulation of cell division (1) | 1.0 |
| 91 | GO:0001837 | 0.067510 | Epithelial to mesenchymal transition (1) | 2.0 |
| 110 | GO:0001942 | 0.066958 | Hair follicle development (1) | 1.0 |
| 275 | GO:0090042 | 0.066526 | Tubulin deacetylation (1) | 1.0 |
| 41 | GO:0007411 | 0.065935 | Axon guidance (1) | 2.0 |
| 679 | GO:0042310 | 0.063952 | Vasoconstriction (1) | 1.0 |
| 232 | GO:0010951 | 0.062732 | Negative regulation of endopeptidase activity (1) | 2.0 |
| 865 | GO:0030217 | 0.062144 | T cell differentiation (1) | 3.0 |
| 171 | GO:0003014 | 0.057983 | Renal system process (1) | 2.0 |
| 501 | GO:0007507 | 0.057228 | Heart development (1) | 4.0 |
| 70 | GO:0001763 | 0.054372 | Morphogenesis of a branching structure (1) | 2.0 |
| 351 | GO:0060048 | 0.052464 | Cardiac muscle contraction (1) | 2.0 |
| 121 | GO:0090050 | 0.049572 | Positive regulation of cell migration involved in sprouting angiogenesis (1) | 0.0 |
| 169 | GO:0002764 | 0.049060 | Immune response-regulating signaling pathway (1) | 3.0 |
| 630 | GO:2000377 | 0.047289 | Regulation of reactive oxygen species metabolic process (1) | 2.0 |
| 537 | GO:0007626 | 0.045891 | Locomotory behavior (1) | 2.0 |
| 799 | GO:0009743 | 0.045252 | Response to carbohydrate (1) | 2.0 |
| 301 | GO:0032388 | 0.044514 | Positive regulation of intracellular transport (1) | 1.0 |
| 40 | GO:0034446 | 0.043394 | Substrate adhesion-dependent cell spreading (1) | 1.0 |
| 152 | GO:0045087 | 0.042899 | Innate immune response (1) | 3.0 |
| 73 | GO:0001779 | 0.041596 | Natural killer cell differentiation (1) | 1.0 |
| 212 | GO:0051090 | 0.040406 | Regulation of dna-binding transcription factor activity (1) | 2.0 |
| 864 | GO:0030183 | 0.038386 | B cell differentiation (1) | 1.0 |
| 705 | GO:0051353 | 0.036453 | Positive regulation of oxidoreductase activity (1) | 1.0 |
| 587 | GO:0050921 | 0.036116 | Positive regulation of chemotaxis (1) | 1.0 |
| 760 | GO:0048286 | 0.035821 | Lung alveolus development (1) | 1.0 |
| 254 | GO:0006687 | 0.035424 | Glycosphingolipid metabolic process (1) | 2.0 |
| 228 | GO:0006508 | 0.033855 | Proteolysis (1) | 4.0 |
| 757 | GO:0021987 | 0.030030 | Cerebral cortex development (1) | 2.0 |
| 673 | GO:0043392 | 0.029313 | Negative regulation of dna binding (1) | 1.0 |
| 530 | GO:0007584 | 0.028301 | Response to nutrient (1) | 3.0 |
| 712 | GO:0033002 | 0.028034 | Muscle cell proliferation (1) | 2.0 |
| 792 | GO:0060560 | 0.027744 | Developmental growth involved in morphogenesis (1) | 3.0 |
| 797 | GO:0071383 | 0.026769 | Cellular response to steroid hormone stimulus (1) | 2.0 |
| 920 | GO:0036092 | 0.025989 | Phosphatidylinositol-3-phosphate biosynthetic process (1) | 0.0 |
| 396 | GO:0007162 | 0.024820 | Negative regulation of cell adhesion (1) | 2.0 |
| 498 | GO:0061029 | 0.024413 | Eyelid development in camera-type eye (1) | 0.0 |
| 480 | GO:0048565 | 0.023576 | Digestive tract development (1) | 1.0 |
| 51 | GO:0045766 | 0.022903 | Positive regulation of angiogenesis (1) | 1.0 |
| 574 | GO:0008285 | 0.020185 | Negative regulation of cell population proliferation (1) | 3.0 |
| 521 | GO:0035051 | 0.019783 | Cardiocyte differentiation (1) | 2.0 |
| 205 | GO:0045740 | 0.019424 | Positive regulation of dna replication (1) | 1.0 |
| 487 | GO:0030325 | 0.018166 | Adrenal gland development (1) | 0.0 |
| 601 | GO:0034103 | 0.018123 | Regulation of tissue remodeling (1) | 1.0 |
| 261 | GO:0051171 | 0.014388 | Regulation of nitrogen compound metabolic process (1) | 6.0 |
| 570 | GO:0045597 | 0.014362 | Positive regulation of cell differentiation (1) | 3.0 |
| 842 | GO:0071407 | 0.014361 | Cellular response to organic cyclic compound (1) | 3.0 |
| 936 | GO:0051000 | 0.014216 | Positive regulation of nitric-oxide synthase activity (1) | 0.0 |
| 461 | GO:0050804 | 0.011722 | Modulation of chemical synaptic transmission (1) | 3.0 |
| 54 | GO:2001214 | 0.010912 | Positive regulation of vasculogenesis (1) | 0.0 |
| 236 | GO:0043161 | 0.009887 | Proteasome-mediated ubiquitin-dependent protein catabolic process (1) | 2.0 |
| 734 | GO:0051702 | 0.006784 | Biological process involved in interaction with symbiont (1) | 2.0 |
| 717 | GO:0046651 | 0.006589 | Lymphocyte proliferation (1) | 2.0 |
| 323 | GO:0016236 | 0.006518 | Macroautophagy (1) | 3.0 |
| 158 | GO:0002862 | 0.006214 | Negative regulation of inflammatory response to antigenic stimulus (1) | 1.0 |
| 619 | GO:0050790 | 0.006102 | Regulation of catalytic activity (1) | 4.0 |
| 520 | GO:0007498 | 0.005849 | Mesoderm development (1) | 3.0 |
| 136 | GO:0002376 | 0.005563 | Immune system process (1) | 6.0 |
| 631 | GO:0050920 | 0.005385 | Regulation of chemotaxis (1) | 2.0 |
| 403 | GO:0033628 | 0.003111 | Regulation of cell adhesion mediated by integrin (1) | 1.0 |
| 345 | GO:0006919 | 0.001149 | Activation of cysteine-type endopeptidase activity involved in apoptotic process (1) | 1.0 |
| 730 | GO:0031175 | 0.000000 | Neuron projection development (1) | 4.0 |
| 304 | GO:0046942 | -0.001338 | Carboxylic acid transport (1) | 3.0 |
| 474 | GO:0030539 | -0.001885 | Male genitalia development (1) | 0.0 |
| 561 | GO:0048511 | -0.003138 | Rhythmic process (1) | 3.0 |
| 736 | GO:0009617 | -0.003666 | Response to bacterium (1) | 3.0 |
| 529 | GO:0007568 | -0.004377 | Aging (1) | 1.0 |
| 747 | GO:0048878 | -0.006747 | Chemical homeostasis (1) | 6.0 |
| 291 | GO:0030072 | -0.007065 | Peptide hormone secretion (1) | 3.0 |
| 580 | GO:0045907 | -0.009718 | Positive regulation of vasoconstriction (1) | 0.0 |
| 467 | GO:0009791 | -0.011347 | Post-embryonic development (1) | 1.0 |
| 935 | GO:0048103 | -0.011384 | Somatic stem cell division (1) | 0.0 |
| 657 | GO:0045165 | -0.012040 | Cell fate commitment (1) | 3.0 |
| 414 | GO:0023019 | -0.016733 | Signal transduction involved in regulation of gene expression (1) | 0.0 |
| 429 | GO:0007229 | -0.017112 | Integrin-mediated signaling pathway (1) | 1.0 |
| 312 | GO:1904062 | -0.018714 | Regulation of cation transmembrane transport (1) | 3.0 |
| 296 | GO:0015031 | -0.019362 | Protein transport (1) | 4.0 |
| 191 | GO:0045944 | -0.020718 | Positive regulation of transcription by rna polymerase ii (1) | 2.0 |
| 602 | GO:0046620 | -0.020955 | Regulation of organ growth (1) | 1.0 |
| 247 | GO:0008610 | -0.024022 | Lipid biosynthetic process (1) | 3.0 |
| 576 | GO:0050866 | -0.024773 | Negative regulation of cell activation (1) | 2.0 |
| 868 | GO:0048863 | -0.027968 | Stem cell differentiation (1) | 2.0 |
| 310 | GO:0051924 | -0.028544 | Regulation of calcium ion transport (1) | 3.0 |
| 332 | GO:0097190 | -0.028734 | Apoptotic signaling pathway (1) | 4.0 |
| 482 | GO:0048557 | -0.029065 | Embryonic digestive tract morphogenesis (1) | 0.0 |
| 262 | GO:0045429 | -0.029754 | Positive regulation of nitric oxide biosynthetic process (1) | 0.0 |
| 622 | GO:0043086 | -0.032086 | Negative regulation of catalytic activity (1) | 3.0 |
| 623 | GO:0031334 | -0.034770 | Positive regulation of protein-containing complex assembly (1) | 2.0 |
| 131 | GO:0002821 | -0.037160 | Positive regulation of adaptive immune response (1) | 2.0 |
| 618 | GO:0032409 | -0.037577 | Regulation of transporter activity (1) | 3.0 |
| 63 | GO:0001666 | -0.040150 | Response to hypoxia (1) | 2.0 |
| 196 | GO:0006357 | -0.041500 | Regulation of transcription by rna polymerase ii (1) | 3.0 |
| 270 | GO:0031056 | -0.041705 | Regulation of histone modification (1) | 2.0 |
| 733 | GO:0030335 | -0.043802 | Positive regulation of cell migration (1) | 3.0 |
| 93 | GO:0001843 | -0.044624 | Neural tube closure (1) | 1.0 |
| 370 | GO:0033044 | -0.044682 | Regulation of chromosome organization (1) | 2.0 |
| 408 | GO:0007165 | -0.044905 | Signal transduction (1) | 6.0 |
| 153 | GO:0050776 | -0.045236 | Regulation of immune response (1) | 4.0 |
| 227 | GO:0032516 | -0.046939 | Positive regulation of phosphoprotein phosphatase activity (1) | 0.0 |
| 217 | GO:0006457 | -0.046986 | Protein folding (1) | 1.0 |
| 415 | GO:0030522 | -0.048782 | Intracellular receptor signaling pathway (1) | 2.0 |
| 366 | GO:0090201 | -0.049666 | Negative regulation of release of cytochrome c from mitochondria (1) | 0.0 |
| 436 | GO:0060079 | -0.050457 | Excitatory postsynaptic potential (1) | 1.0 |
| 380 | GO:0051017 | -0.052351 | Actin filament bundle assembly (1) | 1.0 |
| 903 | GO:0050863 | -0.053759 | Regulation of t cell activation (1) | 3.0 |
| 78 | GO:0032760 | -0.054179 | Positive regulation of tumor necrosis factor production (1) | 0.0 |
| 188 | GO:0009165 | -0.055647 | Nucleotide biosynthetic process (1) | 3.0 |
| 904 | GO:0042113 | -0.058054 | B cell activation (1) | 3.0 |
| 138 | GO:0002683 | -0.058846 | Negative regulation of immune system process (1) | 3.0 |
| 826 | GO:0071364 | -0.059153 | Cellular response to epidermal growth factor stimulus (1) | 0.0 |
| 915 | GO:0050872 | -0.059361 | White fat cell differentiation (1) | 0.0 |
| 102 | GO:0050731 | -0.063114 | Positive regulation of peptidyl-tyrosine phosphorylation (1) | 2.0 |
| 133 | GO:0043303 | -0.063260 | Mast cell degranulation (1) | 1.0 |
| 494 | GO:0060840 | -0.069316 | Artery development (1) | 2.0 |
| 504 | GO:0048709 | -0.071150 | Oligodendrocyte differentiation (1) | 2.0 |
| 178 | GO:0005984 | -0.071330 | Disaccharide metabolic process (1) | 0.0 |
| 737 | GO:0051607 | -0.071636 | Defense response to virus (1) | 2.0 |
| 732 | GO:0060997 | -0.074427 | Dendritic spine morphogenesis (1) | 1.0 |
| 460 | GO:0023061 | -0.075653 | Signal release (1) | 4.0 |
| 67 | GO:0001892 | -0.076334 | Embryonic placenta development (1) | 1.0 |
| 724 | GO:0036473 | -0.088337 | Cell death in response to oxidative stress (1) | 2.0 |
| 447 | GO:0007173 | -0.090517 | Epidermal growth factor receptor signaling pathway (1) | 2.0 |
| 353 | GO:0006954 | -0.092872 | Inflammatory response (1) | 3.0 |
| 861 | GO:0090280 | -0.094645 | Positive regulation of calcium ion import (1) | 0.0 |
| 731 | GO:0031529 | -0.095254 | Ruffle organization (1) | 1.0 |
| 779 | GO:0008544 | -0.096671 | Epidermis development (1) | 3.0 |
| 901 | GO:0042110 | -0.098950 | T cell activation (1) | 4.0 |
| 690 | GO:2001020 | -0.101079 | Regulation of response to dna damage stimulus (1) | 2.0 |
| 360 | GO:0007030 | -0.102202 | Golgi organization (1) | 1.0 |
| 295 | GO:0051047 | -0.107145 | Positive regulation of secretion (1) | 3.0 |
| 786 | GO:0034605 | -0.107349 | Cellular response to heat (1) | 1.0 |
| 749 | GO:0009058 | -0.108343 | Biosynthetic process (1) | 5.0 |
| 688 | GO:1900407 | -0.109537 | Regulation of cellular response to oxidative stress (1) | 2.0 |
| 735 | GO:0046718 | -0.110283 | Viral entry into host cell (1) | 1.0 |
| 76 | GO:0001819 | -0.110409 | Positive regulation of cytokine production (1) | 2.0 |
| 50 | GO:0002040 | -0.110656 | Sprouting angiogenesis (1) | 1.0 |
| 692 | GO:0010595 | -0.112175 | Positive regulation of endothelial cell migration (1) | 2.0 |
| 419 | GO:0003376 | -0.112243 | Sphingosine-1-phosphate receptor signaling pathway (1) | 0.0 |
| 374 | GO:2000251 | -0.113035 | Positive regulation of actin cytoskeleton reorganization (1) | 0.0 |
| 845 | GO:0016071 | -0.114794 | Mrna metabolic process (1) | 4.0 |
| 589 | GO:0042752 | -0.116589 | Regulation of circadian rhythm (1) | 2.0 |
| 615 | GO:0035265 | -0.117147 | Organ growth (1) | 2.0 |
| 918 | GO:0046854 | -0.117793 | Phosphatidylinositol phosphate biosynthetic process (1) | 1.0 |
| 485 | GO:0007422 | -0.118904 | Peripheral nervous system development (1) | 2.0 |
| 81 | GO:0032729 | -0.121441 | Positive regulation of interferon-gamma production (1) | 0.0 |
| 591 | GO:0010941 | -0.121750 | Regulation of cell death (1) | 5.0 |
| 759 | GO:0032835 | -0.121813 | Glomerulus development (1) | 1.0 |
| 511 | GO:0030182 | -0.123468 | Neuron differentiation (1) | 5.0 |
| 260 | GO:0006807 | -0.123894 | Nitrogen compound metabolic process (1) | 7.0 |
| 859 | GO:0021782 | -0.127920 | Glial cell development (1) | 2.0 |
| 108 | GO:0001938 | -0.130000 | Positive regulation of endothelial cell proliferation (1) | 1.0 |
| 335 | GO:2000270 | -0.130342 | Negative regulation of fibroblast apoptotic process (1) | 0.0 |
| 162 | GO:0060374 | -0.130465 | Mast cell differentiation (1) | 0.0 |
| 87 | GO:0072073 | -0.132456 | Kidney epithelium development (1) | 2.0 |
| 183 | GO:0006401 | -0.132593 | Rna catabolic process (1) | 3.0 |
| 818 | GO:0010243 | -0.134405 | Response to organonitrogen compound (1) | 4.0 |
| 641 | GO:0071417 | -0.135033 | Cellular response to organonitrogen compound (1) | 3.0 |
| 464 | GO:0060291 | -0.136241 | Long-term synaptic potentiation (1) | 1.0 |
| 929 | GO:0051341 | -0.137416 | Regulation of oxidoreductase activity (1) | 2.0 |
| 581 | GO:0120162 | -0.138251 | Positive regulation of cold-induced thermogenesis (1) | 0.0 |
| 389 | GO:0010564 | -0.140360 | Regulation of cell cycle process (1) | 4.0 |
| 443 | GO:0035860 | -0.140553 | Glial cell-derived neurotrophic factor receptor signaling pathway (1) | 0.0 |
| 20 | GO:0070507 | -0.141310 | Regulation of microtubule cytoskeleton organization (1) | 2.0 |
| 453 | GO:0007265 | -0.142317 | Ras protein signal transduction (1) | 3.0 |
| 751 | GO:0046034 | -0.143417 | Atp metabolic process (1) | 2.0 |
| 3 | GO:1900087 | -0.145967 | Positive regulation of g1/s transition of mitotic cell cycle (1) | 0.0 |
| 214 | GO:0006412 | -0.146506 | Translation (1) | 4.0 |
| 566 | GO:0032879 | -0.149430 | Regulation of localization (1) | 6.0 |
| 671 | GO:0032092 | -0.152370 | Positive regulation of protein binding (1) | 1.0 |
| 761 | GO:0060021 | -0.155229 | Roof of mouth development (1) | 1.0 |
| 420 | GO:2001241 | -0.155496 | Positive regulation of extrinsic apoptotic signaling pathway in absence of ligand (1) | 0.0 |
| 405 | GO:0007159 | -0.155632 | Leukocyte cell-cell adhesion (1) | 3.0 |
| 358 | GO:0007005 | -0.155655 | Mitochondrion organization (1) | 3.0 |
| 210 | GO:1902275 | -0.156027 | Regulation of chromatin organization (1) | 1.0 |
| 311 | GO:0030001 | -0.158264 | Metal ion transport (1) | 5.0 |
| 810 | GO:0043123 | -0.158702 | Positive regulation of i-kappab kinase/nf-kappab signaling (1) | 0.0 |
| 168 | GO:0045580 | -0.161790 | Regulation of t cell differentiation (1) | 2.0 |
| 72 | GO:0001764 | -0.163226 | Neuron migration (1) | 1.0 |
| 776 | GO:0060644 | -0.163884 | Mammary gland epithelial cell differentiation (1) | 0.0 |
| 319 | GO:0006909 | -0.166043 | Phagocytosis (1) | 2.0 |
| 268 | GO:0016570 | -0.169677 | Histone modification (1) | 4.0 |
| 788 | GO:0009410 | -0.171927 | Response to xenobiotic stimulus (1) | 2.0 |
| 128 | GO:1902036 | -0.173697 | Regulation of hematopoietic stem cell differentiation (1) | 0.0 |
| 609 | GO:0048167 | -0.175411 | Regulation of synaptic plasticity (1) | 2.0 |
| 377 | GO:0031032 | -0.181549 | Actomyosin structure organization (1) | 2.0 |
| 438 | GO:0007179 | -0.182780 | Transforming growth factor beta receptor signaling pathway (1) | 1.0 |
| 28 | GO:0000302 | -0.183602 | Response to reactive oxygen species (1) | 2.0 |
| 745 | GO:0009653 | -0.188122 | Anatomical structure morphogenesis (1) | 5.0 |
| 395 | GO:0007155 | -0.188355 | Cell adhesion (1) | 5.0 |
| 535 | GO:0050910 | -0.188639 | Detection of mechanical stimulus involved in sensory perception of sound (1) | 0.0 |
| 14 | GO:0043407 | -0.189998 | Negative regulation of map kinase activity (1) | 1.0 |
| 636 | GO:0030048 | -0.190360 | Actin filament-based movement (1) | 2.0 |
| 909 | GO:0042593 | -0.191255 | Glucose homeostasis (1) | 2.0 |
| 546 | GO:0008104 | -0.192695 | Protein localization (1) | 5.0 |
| 289 | GO:0007269 | -0.196526 | Neurotransmitter secretion (1) | 2.0 |
| 885 | GO:1900180 | -0.200364 | Regulation of protein localization to nucleus (1) | 1.0 |
| 30 | GO:1901300 | -0.200519 | Positive regulation of hydrogen peroxide-mediated programmed cell death (1) | 0.0 |
| 739 | GO:1902903 | -0.203430 | Regulation of supramolecular fiber organization (1) | 3.0 |
| 847 | GO:0045055 | -0.204997 | Regulated exocytosis (1) | 2.0 |
| 299 | GO:0030705 | -0.206607 | Cytoskeleton-dependent intracellular transport (1) | 3.0 |
| 340 | GO:2001234 | -0.207987 | Negative regulation of apoptotic signaling pathway (1) | 3.0 |
| 849 | GO:0043966 | -0.208541 | Histone h3 acetylation (1) | 2.0 |
| 286 | GO:0055085 | -0.210703 | Transmembrane transport (1) | 6.0 |
| 363 | GO:0051494 | -0.211867 | Negative regulation of cytoskeleton organization (1) | 2.0 |
| 48 | GO:0001525 | -0.211963 | Angiogenesis (1) | 2.0 |
| 813 | GO:0051898 | -0.215146 | Negative regulation of protein kinase b signaling (1) | 0.0 |
| 700 | GO:0033690 | -0.223856 | Positive regulation of osteoblast proliferation (1) | 0.0 |
| 209 | GO:0006338 | -0.224254 | Chromatin remodeling (1) | 2.0 |
| 416 | GO:0035556 | -0.225162 | Intracellular signal transduction (1) | 4.0 |
| 916 | GO:2000739 | -0.225462 | Regulation of mesenchymal stem cell differentiation (1) | 0.0 |
| 79 | GO:0032740 | -0.225949 | Positive regulation of interleukin-17 production (1) | 0.0 |
| 572 | GO:0034767 | -0.226631 | Positive regulation of ion transmembrane transport (1) | 2.0 |
| 272 | GO:0031398 | -0.229217 | Positive regulation of protein ubiquitination (1) | 2.0 |
| 222 | GO:0018107 | -0.231930 | Peptidyl-threonine phosphorylation (1) | 1.0 |
| 879 | GO:0031103 | -0.232786 | Axon regeneration (1) | 1.0 |
| 413 | GO:0009968 | -0.233227 | Negative regulation of signal transduction (1) | 4.0 |
| 765 | GO:0021795 | -0.237208 | Cerebral cortex cell migration (1) | 1.0 |
| 325 | GO:0006915 | -0.240649 | Apoptotic process (1) | 5.0 |
| 172 | GO:0003158 | -0.242769 | Endothelium development (1) | 2.0 |
| 357 | GO:0006997 | -0.247765 | Nucleus organization (1) | 2.0 |
| 725 | GO:0070997 | -0.248013 | Neuron death (1) | 2.0 |
| 355 | GO:0050729 | -0.250210 | Positive regulation of inflammatory response (1) | 1.0 |
| 233 | GO:0016579 | -0.250907 | Protein deubiquitination (1) | 1.0 |
| 666 | GO:0010727 | -0.258854 | Negative regulation of hydrogen peroxide metabolic process (1) | 0.0 |
| 202 | GO:0006753 | -0.259533 | Nucleoside phosphate metabolic process (1) | 4.0 |
| 685 | GO:0051973 | -0.259761 | Positive regulation of telomerase activity (1) | 0.0 |
| 549 | GO:0009306 | -0.261559 | Protein secretion (1) | 3.0 |
| 778 | GO:0045793 | -0.270168 | Positive regulation of cell size (1) | 0.0 |
| 303 | GO:1904659 | -0.270188 | Glucose transmembrane transport (1) | 1.0 |
| 676 | GO:0030282 | -0.272104 | Bone mineralization (1) | 1.0 |
| 294 | GO:0051046 | -0.276779 | Regulation of secretion (1) | 4.0 |
| 362 | GO:0033043 | -0.280077 | Regulation of organelle organization (1) | 4.0 |
| 123 | GO:0002064 | -0.285938 | Epithelial cell development (1) | 2.0 |
| 282 | GO:0016192 | -0.287866 | Vesicle-mediated transport (1) | 4.0 |
| 678 | GO:0071333 | -0.289435 | Cellular response to glucose stimulus (1) | 1.0 |
| 523 | GO:0007517 | -0.293992 | Muscle organ development (1) | 3.0 |
| 150 | GO:0002437 | -0.297868 | Inflammatory response to antigenic stimulus (1) | 2.0 |
| 704 | GO:1904707 | -0.299166 | Positive regulation of vascular associated smooth muscle cell proliferation (1) | 0.0 |
| 771 | GO:0031016 | -0.299979 | Pancreas development (1) | 2.0 |
| 937 | GO:0051640 | -0.307828 | Organelle localization (1) | 3.0 |
| 107 | GO:1905563 | -0.308080 | Negative regulation of vascular endothelial cell proliferation (1) | 0.0 |
| 404 | GO:0007156 | -0.311359 | Homophilic cell adhesion via plasma membrane adhesion molecules (1) | 1.0 |
| 238 | GO:0043162 | -0.311445 | Ubiquitin-dependent protein catabolic process via the multivesicular body sorting pathway (1) | 1.0 |
| 752 | GO:0043170 | -0.315334 | Macromolecule metabolic process (1) | 7.0 |
| 552 | GO:0033365 | -0.319737 | Protein localization to organelle (1) | 3.0 |
| 881 | GO:0031929 | -0.320242 | Tor signaling (1) | 2.0 |
| 484 | GO:0007399 | -0.320323 | Nervous system development (1) | 6.0 |
| 129 | GO:0002250 | -0.320676 | Adaptive immune response (1) | 4.0 |
| 741 | GO:0016477 | -0.323694 | Cell migration (1) | 4.0 |
| 600 | GO:0010632 | -0.324795 | Regulation of epithelial cell migration (1) | 3.0 |
| 197 | GO:0006367 | -0.325397 | Transcription initiation from rna polymerase ii promoter (1) | 2.0 |
| 118 | GO:0060571 | -0.325595 | Morphogenesis of an epithelial fold (1) | 1.0 |
| 505 | GO:0007283 | -0.329296 | Spermatogenesis (1) | 2.0 |
| 542 | GO:0048149 | -0.329477 | Behavioral response to ethanol (1) | 0.0 |
| 391 | GO:0090068 | -0.333050 | Positive regulation of cell cycle process (1) | 2.0 |
| 656 | GO:0030154 | -0.339581 | Cell differentiation (1) | 6.0 |
| 698 | GO:0070663 | -0.341786 | Regulation of leukocyte proliferation (1) | 2.0 |
| 164 | GO:0051249 | -0.342420 | Regulation of lymphocyte activation (1) | 4.0 |
| 720 | GO:0051261 | -0.344762 | Protein depolymerization (1) | 2.0 |
| 475 | GO:0050769 | -0.344990 | Positive regulation of neurogenesis (1) | 2.0 |
| 165 | GO:0050870 | -0.345649 | Positive regulation of t cell activation (1) | 2.0 |
| 432 | GO:0038007 | -0.346961 | Netrin-activated signaling pathway (1) | 0.0 |
| 246 | GO:0008202 | -0.348429 | Steroid metabolic process (1) | 3.0 |
| 831 | GO:0001975 | -0.357673 | Response to amphetamine (1) | 0.0 |
| 177 | GO:0045821 | -0.361584 | Positive regulation of glycolytic process (1) | 0.0 |
| 127 | GO:0002320 | -0.363702 | Lymphoid progenitor cell differentiation (1) | 1.0 |
| 399 | GO:0098609 | -0.364601 | Cell-cell adhesion (1) | 4.0 |
| 599 | GO:0017157 | -0.372042 | Regulation of exocytosis (1) | 2.0 |
| 790 | GO:0034644 | -0.372186 | Cellular response to uv (1) | 1.0 |
| 568 | GO:0008284 | -0.372632 | Positive regulation of cell population proliferation (1) | 2.0 |
| 75 | GO:0001818 | -0.372931 | Negative regulation of cytokine production (1) | 1.0 |
| 610 | GO:0031333 | -0.373027 | Negative regulation of protein-containing complex assembly (1) | 2.0 |
| 770 | GO:0009887 | -0.378818 | Animal organ morphogenesis (1) | 4.0 |
| 18 | GO:0000226 | -0.382412 | Microtubule cytoskeleton organization (1) | 3.0 |
| 244 | GO:0042307 | -0.383034 | Positive regulation of protein import into nucleus (1) | 0.0 |
| 510 | GO:0042063 | -0.386958 | Gliogenesis (1) | 3.0 |
| 252 | GO:0046889 | -0.389004 | Positive regulation of lipid biosynthetic process (1) | 1.0 |
| 683 | GO:0048477 | -0.389075 | Oogenesis (1) | 1.0 |
| 686 | GO:0097009 | -0.390741 | Energy homeostasis (1) | 0.0 |
| 597 | GO:0060627 | -0.391978 | Regulation of vesicle-mediated transport (1) | 3.0 |
| 594 | GO:0051128 | -0.392248 | Regulation of cellular component organization (1) | 5.0 |
| 36 | GO:0000723 | -0.397902 | Telomere maintenance (1) | 1.0 |
| 579 | GO:0040018 | -0.399025 | Positive regulation of multicellular organism growth (1) | 0.0 |
| 55 | GO:0001649 | -0.403752 | Osteoblast differentiation (1) | 1.0 |
| 642 | GO:0034599 | -0.410954 | Cellular response to oxidative stress (1) | 3.0 |
| 318 | GO:0006898 | -0.412020 | Receptor-mediated endocytosis (1) | 2.0 |
| 518 | GO:0042472 | -0.414984 | Inner ear morphogenesis (1) | 1.0 |
| 816 | GO:0032008 | -0.416975 | Positive regulation of tor signaling (1) | 1.0 |
| 808 | GO:1902532 | -0.418085 | Negative regulation of intracellular signal transduction (1) | 3.0 |
| 135 | GO:0042093 | -0.420233 | T-helper cell differentiation (1) | 1.0 |
| 364 | GO:0140013 | -0.421435 | Meiotic nuclear division (1) | 2.0 |
| 536 | GO:0007610 | -0.428170 | Behavior (1) | 3.0 |
| 267 | GO:0045732 | -0.434807 | Positive regulation of protein catabolic process (1) | 2.0 |
| 789 | GO:0009416 | -0.439464 | Response to light stimulus (1) | 2.0 |
| 637 | GO:0030198 | -0.439683 | Extracellular matrix organization (1) | 2.0 |
| 82 | GO:0032743 | -0.458060 | Positive regulation of interleukin-2 production (1) | 0.0 |
| 694 | GO:0032869 | -0.463421 | Cellular response to insulin stimulus (1) | 2.0 |
| 646 | GO:0071320 | -0.466282 | Cellular response to camp (1) | 0.0 |
| 146 | GO:0043029 | -0.470875 | T cell homeostasis (1) | 1.0 |
| 911 | GO:0060416 | -0.475618 | Response to growth hormone (1) | 1.0 |
| 237 | GO:0006511 | -0.479298 | Ubiquitin-dependent protein catabolic process (1) | 3.0 |
| 819 | GO:0014070 | -0.480176 | Response to organic cyclic compound (1) | 4.0 |
| 896 | GO:0097193 | -0.483526 | Intrinsic apoptotic signaling pathway (1) | 3.0 |
| 134 | GO:0002366 | -0.488727 | Leukocyte activation involved in immune response (1) | 3.0 |
| 493 | GO:0001946 | -0.489050 | Lymphangiogenesis (1) | 0.0 |
| 689 | GO:1905897 | -0.495409 | Regulation of response to endoplasmic reticulum stress (1) | 2.0 |
| 478 | GO:0048568 | -0.497049 | Embryonic organ development (1) | 3.0 |
| 820 | GO:0033993 | -0.497561 | Response to lipid (1) | 3.0 |
| 934 | GO:0051258 | -0.500444 | Protein polymerization (1) | 3.0 |
| 800 | GO:0030521 | -0.503978 | Androgen receptor signaling pathway (1) | 1.0 |
| 276 | GO:0016567 | -0.510030 | Protein ubiquitination (1) | 3.0 |
| 663 | GO:0050821 | -0.512838 | Protein stabilization (1) | 0.0 |
| 768 | GO:0051147 | -0.515025 | Regulation of muscle cell differentiation (1) | 2.0 |
| 384 | GO:0007018 | -0.516240 | Microtubule-based movement (1) | 4.0 |
| 200 | GO:0006281 | -0.520134 | Dna repair (1) | 2.0 |
| 496 | GO:0048608 | -0.521065 | Reproductive structure development (1) | 2.0 |
| 921 | GO:0035790 | -0.529398 | Platelet-derived growth factor receptor-alpha signaling pathway (1) | 0.0 |
| 207 | GO:0006303 | -0.530286 | Double-strand break repair via nonhomologous end joining (1) | 1.0 |
| 548 | GO:1903829 | -0.534357 | Positive regulation of protein localization (1) | 3.0 |
| 889 | GO:0098780 | -0.534495 | Response to mitochondrial depolarisation (1) | 1.0 |
| 281 | GO:0006811 | -0.536119 | Ion transport (1) | 6.0 |
| 336 | GO:0043524 | -0.548174 | Negative regulation of neuron apoptotic process (1) | 1.0 |
| 184 | GO:0006275 | -0.549978 | Regulation of dna replication (1) | 2.0 |
| 96 | GO:0048873 | -0.558233 | Homeostasis of number of cells within a tissue (1) | 0.0 |
| 449 | GO:0007204 | -0.558363 | Positive regulation of cytosolic calcium ion concentration (1) | 4.0 |
| 744 | GO:0055082 | -0.561669 | Cellular chemical homeostasis (1) | 5.0 |
| 329 | GO:0043066 | -0.563158 | Negative regulation of apoptotic process (1) | 4.0 |
| 37 | GO:0000724 | -0.565085 | Double-strand break repair via homologous recombination (1) | 1.0 |
| 871 | GO:0042551 | -0.565380 | Neuron maturation (1) | 1.0 |
| 525 | GO:0048741 | -0.566143 | Skeletal muscle fiber development (1) | 1.0 |
| 557 | GO:0072655 | -0.572037 | Establishment of protein localization to mitochondrion (1) | 1.0 |
| 215 | GO:0006417 | -0.572163 | Regulation of translation (1) | 3.0 |
| 584 | GO:0040008 | -0.572873 | Regulation of growth (1) | 3.0 |
| 748 | GO:0009056 | -0.572913 | Catabolic process (1) | 5.0 |
| 556 | GO:0051223 | -0.574651 | Regulation of protein transport (1) | 3.0 |
| 433 | GO:0097191 | -0.576815 | Extrinsic apoptotic signaling pathway (1) | 3.0 |
| 502 | GO:0007420 | -0.577438 | Brain development (1) | 4.0 |
| 371 | GO:0010821 | -0.581599 | Regulation of mitochondrion organization (1) | 2.0 |
| 326 | GO:0008637 | -0.587399 | Apoptotic mitochondrial changes (1) | 1.0 |
| 767 | GO:0051146 | -0.588274 | Striated muscle cell differentiation (1) | 2.0 |
| 314 | GO:0070588 | -0.591082 | Calcium ion transmembrane transport (1) | 4.0 |
| 248 | GO:0016042 | -0.605006 | Lipid catabolic process (1) | 2.0 |
| 411 | GO:0009755 | -0.607054 | Hormone-mediated signaling pathway (1) | 2.0 |
| 182 | GO:0016070 | -0.607882 | Rna metabolic process (1) | 5.0 |
| 888 | GO:0034976 | -0.613362 | Response to endoplasmic reticulum stress (1) | 3.0 |
| 176 | GO:0044262 | -0.620829 | Cellular carbohydrate metabolic process (1) | 3.0 |
| 846 | GO:0099504 | -0.627336 | Synaptic vesicle cycle (1) | 2.0 |
| 528 | GO:0007565 | -0.627705 | Female pregnancy (1) | 2.0 |
| 211 | GO:0031507 | -0.627760 | Heterochromatin assembly (1) | 1.0 |
| 94 | GO:0001889 | -0.633760 | Liver development (1) | 1.0 |
| 52 | GO:0001541 | -0.634171 | Ovarian follicle development (1) | 1.0 |
| 137 | GO:0002682 | -0.635094 | Regulation of immune system process (1) | 5.0 |
| 620 | GO:0051098 | -0.635782 | Regulation of binding (1) | 3.0 |
| 266 | GO:0030163 | -0.652510 | Protein catabolic process (1) | 4.0 |
| 638 | GO:0033554 | -0.652740 | Cellular response to stress (1) | 4.0 |
| 777 | GO:0050680 | -0.661189 | Negative regulation of epithelial cell proliferation (1) | 2.0 |
| 425 | GO:0060391 | -0.664363 | Positive regulation of smad protein signal transduction (1) | 0.0 |
| 375 | GO:0051496 | -0.667875 | Positive regulation of stress fiber assembly (1) | 0.0 |
| 85 | GO:0001823 | -0.670272 | Mesonephros development (1) | 2.0 |
| 654 | GO:0090398 | -0.671529 | Cellular senescence (1) | 1.0 |
| 774 | GO:0030216 | -0.675144 | Keratinocyte differentiation (1) | 2.0 |
| 316 | GO:0033157 | -0.678469 | Regulation of intracellular protein transport (1) | 1.0 |
| 693 | GO:1904646 | -0.683366 | Cellular response to amyloid-beta (1) | 0.0 |
| 186 | GO:2000278 | -0.687722 | Regulation of dna biosynthetic process (1) | 1.0 |
| 418 | GO:2001240 | -0.689139 | Negative regulation of extrinsic apoptotic signaling pathway in absence of ligand (1) | 0.0 |
| 117 | GO:0060562 | -0.691717 | Epithelial tube morphogenesis (1) | 2.0 |
| 644 | GO:0071230 | -0.699092 | Cellular response to amino acid stimulus (1) | 1.0 |
| 298 | GO:0006886 | -0.703192 | Intracellular protein transport (1) | 3.0 |
| 473 | GO:0008584 | -0.709411 | Male gonad development (1) | 1.0 |
| 687 | GO:0072384 | -0.726414 | Organelle transport along microtubule (1) | 2.0 |
| 785 | GO:0009266 | -0.729968 | Response to temperature stimulus (1) | 2.0 |
| 116 | GO:0002009 | -0.733249 | Morphogenesis of an epithelium (1) | 3.0 |
| 867 | GO:0045444 | -0.735911 | Fat cell differentiation (1) | 2.0 |
| 564 | GO:0045596 | -0.736029 | Negative regulation of cell differentiation (1) | 2.0 |
| 804 | GO:0030855 | -0.743127 | Epithelial cell differentiation (1) | 3.0 |
| 887 | GO:0034504 | -0.747907 | Protein localization to nucleus (1) | 2.0 |
| 64 | GO:0071456 | -0.749105 | Cellular response to hypoxia (1) | 1.0 |
| 173 | GO:0003300 | -0.749363 | Cardiac muscle hypertrophy (1) | 2.0 |
| 422 | GO:0046628 | -0.749367 | Positive regulation of insulin receptor signaling pathway (1) | 0.0 |
| 783 | GO:0008630 | -0.759892 | Intrinsic apoptotic signaling pathway in response to dna damage (1) | 2.0 |
| 84 | GO:0001822 | -0.761027 | Kidney development (1) | 3.0 |
| 208 | GO:0006325 | -0.765908 | Chromatin organization (1) | 3.0 |
| 349 | GO:0055118 | -0.766833 | Negative regulation of cardiac muscle contraction (1) | 0.0 |
| 497 | GO:0060041 | -0.772237 | Retina development in camera-type eye (1) | 1.0 |
| 836 | GO:1901987 | -0.773018 | Regulation of cell cycle phase transition (1) | 3.0 |
| 850 | GO:0070932 | -0.775462 | Histone h3 deacetylation (1) | 0.0 |
| 522 | GO:0048738 | -0.777561 | Cardiac muscle tissue development (1) | 2.0 |
| 26 | GO:1901990 | -0.788841 | Regulation of mitotic cell cycle phase transition (1) | 2.0 |
| 606 | GO:0048638 | -0.795928 | Regulation of developmental growth (1) | 2.0 |
| 143 | GO:0038096 | -0.798475 | Fc-gamma receptor signaling pathway involved in phagocytosis (1) | 0.0 |
| 649 | GO:0071478 | -0.804830 | Cellular response to radiation (1) | 2.0 |
| 775 | GO:0022612 | -0.808633 | Gland morphogenesis (1) | 2.0 |
| 92 | GO:0010718 | -0.808642 | Positive regulation of epithelial to mesenchymal transition (1) | 1.0 |
| 534 | GO:0030193 | -0.809407 | Regulation of blood coagulation (1) | 2.0 |
| 185 | GO:0051054 | -0.817126 | Positive regulation of dna metabolic process (1) | 2.0 |
| 930 | GO:1902074 | -0.824018 | Response to salt (1) | 1.0 |
| 514 | GO:0021575 | -0.825947 | Hindbrain morphogenesis (1) | 1.0 |
| 876 | GO:0046632 | -0.839055 | Alpha-beta t cell differentiation (1) | 2.0 |
| 558 | GO:0016032 | -0.841189 | Viral process (1) | 3.0 |
| 490 | GO:0007435 | -0.854168 | Salivary gland morphogenesis (1) | 1.0 |
| 359 | GO:0007010 | -0.858980 | Cytoskeleton organization (1) | 4.0 |
| 718 | GO:0008361 | -0.861438 | Regulation of cell size (1) | 2.0 |
| 801 | GO:0033143 | -0.875603 | Regulation of intracellular steroid hormone receptor signaling pathway (1) | 1.0 |
| 179 | GO:0006096 | -0.888237 | Glycolytic process (1) | 1.0 |
| 157 | GO:0048538 | -0.899567 | Thymus development (1) | 0.0 |
| 886 | GO:0034502 | -0.908113 | Protein localization to chromosome (1) | 2.0 |
| 647 | GO:0071392 | -0.910313 | Cellular response to estradiol stimulus (1) | 0.0 |
| 457 | GO:0035022 | -0.913306 | Positive regulation of rac protein signal transduction (1) | 0.0 |
| 56 | GO:0045668 | -0.913637 | Negative regulation of osteoblast differentiation (1) | 0.0 |
| 843 | GO:0014823 | -0.918185 | Response to activity (1) | 1.0 |
| 376 | GO:0007015 | -0.919887 | Actin filament organization (1) | 3.0 |
| 166 | GO:0030890 | -0.921340 | Positive regulation of b cell proliferation (1) | 0.0 |
| 526 | GO:0048743 | -0.926052 | Positive regulation of skeletal muscle fiber development (1) | 0.0 |
| 21 | GO:0051225 | -0.929849 | Spindle assembly (1) | 2.0 |
| 596 | GO:0060341 | -0.932901 | Regulation of cellular localization (1) | 3.0 |
| 140 | GO:0045321 | -0.933341 | Leukocyte activation (1) | 5.0 |
| 855 | GO:1903578 | -0.938108 | Regulation of atp metabolic process (1) | 1.0 |
| 100 | GO:0033138 | -0.947197 | Positive regulation of peptidyl-serine phosphorylation (1) | 1.0 |
| 34 | GO:1903146 | -0.948778 | Regulation of autophagy of mitochondrion (1) | 1.0 |
| 120 | GO:1905278 | -0.954451 | Positive regulation of epithelial tube formation (1) | 0.0 |
| 726 | GO:0065003 | -0.957268 | Protein-containing complex assembly (1) | 4.0 |
| 330 | GO:0071839 | -0.961212 | Apoptotic process in bone marrow cell (1) | 0.0 |
| 902 | GO:0046631 | -0.968398 | Alpha-beta t cell activation (1) | 3.0 |
| 621 | GO:0032410 | -0.978890 | Negative regulation of transporter activity (1) | 1.0 |
| 17 | GO:0000209 | -0.995522 | Protein polyubiquitination (1) | 2.0 |
| 840 | GO:0043154 | -0.996503 | Negative regulation of cysteine-type endopeptidase activity involved in apoptotic process (1) | 1.0 |
| 35 | GO:0061734 | -0.998003 | Parkin-mediated stimulation of mitophagy in response to mitochondrial depolarization (1) | 0.0 |
| 387 | GO:0051321 | -1.010241 | Meiotic cell cycle (1) | 3.0 |
| 6 | GO:0010971 | -1.013845 | Positive regulation of g2/m transition of mitotic cell cycle (1) | 0.0 |
| 488 | GO:0030878 | -1.022587 | Thyroid gland development (1) | 0.0 |
| 598 | GO:0043254 | -1.024401 | Regulation of protein-containing complex assembly (1) | 3.0 |
| 161 | GO:0030316 | -1.031163 | Osteoclast differentiation (1) | 2.0 |
| 315 | GO:0046902 | -1.038293 | Regulation of mitochondrial membrane permeability (1) | 1.0 |
| 758 | GO:0031099 | -1.044014 | Regeneration (1) | 2.0 |
| 356 | GO:0006996 | -1.050513 | Organelle organization (1) | 5.0 |
| 547 | GO:0032880 | -1.061375 | Regulation of protein localization (1) | 4.0 |
| 470 | GO:0042733 | -1.062045 | Embryonic digit morphogenesis (1) | 0.0 |
| 590 | GO:0050792 | -1.063199 | Regulation of viral process (1) | 2.0 |
| 875 | GO:0033077 | -1.072052 | T cell differentiation in thymus (1) | 1.0 |
| 25 | GO:0045930 | -1.075304 | Negative regulation of mitotic cell cycle (1) | 2.0 |
| 293 | GO:0032024 | -1.085745 | Positive regulation of insulin secretion (1) | 1.0 |
| 305 | GO:0034220 | -1.105994 | Ion transmembrane transport (1) | 5.0 |
| 279 | GO:0018205 | -1.119880 | Peptidyl-lysine modification (1) | 4.0 |
| 300 | GO:0032386 | -1.120402 | Regulation of intracellular transport (1) | 2.0 |
| 19 | GO:0031109 | -1.121358 | Microtubule polymerization or depolymerization (1) | 2.0 |
| 302 | GO:0006869 | -1.132701 | Lipid transport (1) | 3.0 |
| 368 | GO:0060271 | -1.144707 | Cilium assembly (1) | 3.0 |
| 258 | GO:0006694 | -1.145934 | Steroid biosynthetic process (1) | 2.0 |
| 571 | GO:2000010 | -1.155468 | Positive regulation of protein localization to cell surface (1) | 0.0 |
| 880 | GO:0031667 | -1.156877 | Response to nutrient levels (1) | 4.0 |
| 220 | GO:0016572 | -1.176296 | Histone phosphorylation (1) | 1.0 |
| 805 | GO:0090090 | -1.179703 | Negative regulation of canonical wnt signaling pathway (1) | 0.0 |
| 856 | GO:0019722 | -1.180979 | Calcium-mediated signaling (1) | 2.0 |
| 481 | GO:0030324 | -1.181819 | Lung development (1) | 2.0 |
| 927 | GO:0042632 | -1.192404 | Cholesterol homeostasis (1) | 0.0 |
| 334 | GO:2000811 | -1.206493 | Negative regulation of anoikis (1) | 0.0 |
| 696 | GO:0030336 | -1.218248 | Negative regulation of cell migration (1) | 2.0 |
| 891 | GO:0034405 | -1.223979 | Response to fluid shear stress (1) | 1.0 |
| 61 | GO:0001658 | -1.225087 | Branching involved in ureteric bud morphogenesis (1) | 1.0 |
| 672 | GO:0032091 | -1.230462 | Negative regulation of protein binding (1) | 1.0 |
| 292 | GO:0046883 | -1.239220 | Regulation of hormone secretion (1) | 3.0 |
| 585 | GO:0048589 | -1.242926 | Developmental growth (1) | 4.0 |
| 634 | GO:0061024 | -1.243050 | Membrane organization (1) | 2.0 |
| 324 | GO:0016241 | -1.250511 | Regulation of macroautophagy (1) | 2.0 |
| 554 | GO:0072659 | -1.270281 | Protein localization to plasma membrane (1) | 2.0 |
| 476 | GO:0048714 | -1.271201 | Positive regulation of oligodendrocyte differentiation (1) | 0.0 |
| 603 | GO:0061045 | -1.272009 | Negative regulation of wound healing (1) | 2.0 |
| 917 | GO:1900020 | -1.277862 | Positive regulation of protein kinase c activity (1) | 0.0 |
| 407 | GO:0030010 | -1.292763 | Establishment of cell polarity (1) | 1.0 |
| 559 | GO:0022414 | -1.306268 | Reproductive process (1) | 4.0 |
| 199 | GO:0006270 | -1.310311 | Dna replication initiation (1) | 1.0 |
| 327 | GO:0033028 | -1.310645 | Myeloid cell apoptotic process (1) | 1.0 |
| 221 | GO:0018105 | -1.313496 | Peptidyl-serine phosphorylation (1) | 2.0 |
| 882 | GO:0032147 | -1.314908 | Activation of protein kinase activity (1) | 1.0 |
| 297 | GO:0006839 | -1.314960 | Mitochondrial transport (1) | 2.0 |
| 773 | GO:0060612 | -1.316095 | Adipose tissue development (1) | 1.0 |
| 815 | GO:1901224 | -1.319667 | Positive regulation of nik/nf-kappab signaling (1) | 0.0 |
| 551 | GO:1903077 | -1.319677 | Negative regulation of protein localization to plasma membrane (1) | 1.0 |
| 149 | GO:0050853 | -1.320551 | B cell receptor signaling pathway (1) | 1.0 |
| 807 | GO:1901796 | -1.330292 | Regulation of signal transduction by p53 class mediator (1) | 1.0 |
| 682 | GO:0007026 | -1.340212 | Negative regulation of microtubule depolymerization (1) | 0.0 |
| 515 | GO:0021549 | -1.341320 | Cerebellum development (1) | 2.0 |
| 798 | GO:0051384 | -1.346453 | Response to glucocorticoid (1) | 1.0 |
| 390 | GO:0044770 | -1.348006 | Cell cycle phase transition (1) | 4.0 |
| 198 | GO:0006260 | -1.359349 | Dna replication (1) | 3.0 |
| 614 | GO:0035264 | -1.365256 | Multicellular organism growth (1) | 1.0 |
| 5 | GO:0000086 | -1.366187 | G2/m transition of mitotic cell cycle (1) | 1.0 |
| 628 | GO:0043467 | -1.377923 | Regulation of generation of precursor metabolites and energy (1) | 2.0 |
| 616 | GO:0001556 | -1.391091 | Oocyte maturation (1) | 0.0 |
| 201 | GO:0006310 | -1.394633 | Dna recombination (1) | 3.0 |
| 243 | GO:0006606 | -1.399288 | Protein import into nucleus (1) | 1.0 |
| 104 | GO:0006469 | -1.403847 | Negative regulation of protein kinase activity (1) | 2.0 |
| 784 | GO:0042771 | -1.409468 | Intrinsic apoptotic signaling pathway in response to dna damage by p53 class mediator (1) | 1.0 |
| 204 | GO:0006261 | -1.419899 | Dna-dependent dna replication (1) | 2.0 |
| 2 | GO:0000082 | -1.425082 | G1/s transition of mitotic cell cycle (1) | 2.0 |
| 878 | GO:0030593 | -1.429383 | Neutrophil chemotaxis (1) | 1.0 |
| 839 | GO:0031047 | -1.438952 | Gene silencing by rna (1) | 2.0 |
| 15 | GO:0070373 | -1.439598 | Negative regulation of erk1 and erk2 cascade (1) | 0.0 |
| 367 | GO:1901029 | -1.442702 | Negative regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway (1) | 0.0 |
| 385 | GO:0060632 | -1.444681 | Regulation of microtubule-based movement (1) | 1.0 |
| 285 | GO:0051051 | -1.447128 | Negative regulation of transport (1) | 3.0 |
| 1 | GO:0045737 | -1.452510 | Positive regulation of cyclin-dependent protein serine/threonine kinase activity (1) | 0.0 |
| 928 | GO:0051453 | -1.491236 | Regulation of intracellular ph (1) | 1.0 |
| 569 | GO:0030307 | -1.507144 | Positive regulation of cell growth (1) | 2.0 |
| 264 | GO:0042177 | -1.517738 | Negative regulation of protein catabolic process (1) | 1.0 |
| 442 | GO:0008286 | -1.524273 | Insulin receptor signaling pathway (1) | 1.0 |
| 524 | GO:0007519 | -1.531881 | Skeletal muscle tissue development (1) | 2.0 |
| 65 | GO:0001701 | -1.542908 | In utero embryonic development (1) | 2.0 |
| 174 | GO:0010613 | -1.546047 | Positive regulation of cardiac muscle hypertrophy (1) | 1.0 |
| 167 | GO:0045637 | -1.553460 | Regulation of myeloid cell differentiation (1) | 2.0 |
| 213 | GO:0006396 | -1.579762 | Rna processing (1) | 4.0 |
| 925 | GO:1990403 | -1.599465 | Embryonic brain development (1) | 0.0 |
| 62 | GO:0001662 | -1.634974 | Behavioral fear response (1) | 1.0 |
| 112 | GO:0060789 | -1.642170 | Hair follicle placode formation (1) | 0.0 |
| 321 | GO:0010507 | -1.662573 | Negative regulation of autophagy (1) | 1.0 |
| 193 | GO:0006368 | -1.663610 | Transcription elongation from rna polymerase ii promoter (1) | 1.0 |
| 910 | GO:0051354 | -1.665789 | Negative regulation of oxidoreductase activity (1) | 1.0 |
| 648 | GO:0071549 | -1.672280 | Cellular response to dexamethasone stimulus (1) | 0.0 |
| 240 | GO:0006605 | -1.701265 | Protein targeting (1) | 2.0 |
| 251 | GO:0045833 | -1.715199 | Negative regulation of lipid metabolic process (1) | 2.0 |
| 341 | GO:2001236 | -1.716145 | Regulation of extrinsic apoptotic signaling pathway (1) | 2.0 |
| 322 | GO:0010508 | -1.734826 | Positive regulation of autophagy (1) | 2.0 |
| 344 | GO:1902236 | -1.763757 | Negative regulation of endoplasmic reticulum stress-induced intrinsic apoptotic signaling pathway (1) | 0.0 |
| 828 | GO:0010039 | -1.764349 | Response to iron ion (1) | 1.0 |
| 577 | GO:2001021 | -1.770162 | Negative regulation of response to dna damage stimulus (1) | 1.0 |
| 342 | GO:2001243 | -1.793574 | Negative regulation of intrinsic apoptotic signaling pathway (1) | 2.0 |
| 830 | GO:0071480 | -1.811677 | Cellular response to gamma radiation (1) | 0.0 |
| 241 | GO:0006612 | -1.832631 | Protein targeting to membrane (1) | 1.0 |
| 550 | GO:1904950 | -1.845239 | Negative regulation of establishment of protein localization (1) | 2.0 |
| 586 | GO:2000773 | -1.847992 | Negative regulation of cellular senescence (1) | 0.0 |
| 33 | GO:0000423 | -1.852021 | Mitophagy (1) | 1.0 |
| 145 | GO:0001782 | -1.865421 | B cell homeostasis (1) | 0.0 |
| 527 | GO:0007528 | -1.888030 | Neuromuscular junction development (1) | 1.0 |
| 931 | GO:0002931 | -1.889223 | Response to ischemia (1) | 0.0 |
| 111 | GO:0031069 | -1.907723 | Hair follicle morphogenesis (1) | 0.0 |
| 575 | GO:0030308 | -1.913032 | Negative regulation of cell growth (1) | 1.0 |
| 393 | GO:1901988 | -1.952405 | Negative regulation of cell cycle phase transition (1) | 2.0 |
| 68 | GO:0042659 | -1.958074 | Regulation of cell fate specification (1) | 0.0 |
| 834 | GO:0035195 | -1.964770 | Gene silencing by mirna (1) | 1.0 |
| 271 | GO:2000757 | -1.982632 | Negative regulation of peptidyl-lysine acetylation (1) | 1.0 |
| 451 | GO:0007263 | -1.986508 | Nitric oxide mediated signal transduction (1) | 1.0 |
| 677 | GO:0010977 | -2.012737 | Negative regulation of neuron projection development (1) | 1.0 |
| 822 | GO:0046898 | -2.020251 | Response to cycloheximide (1) | 0.0 |
| 893 | GO:0035094 | -2.023491 | Response to nicotine (1) | 1.0 |
| 635 | GO:0099173 | -2.072765 | Postsynapse organization (1) | 2.0 |
| 884 | GO:0032469 | -2.081057 | Endoplasmic reticulum calcium ion homeostasis (1) | 1.0 |
| 538 | GO:0048266 | -2.139693 | Behavioral response to pain (1) | 0.0 |
| 787 | GO:0042149 | -2.188398 | Cellular response to glucose starvation (1) | 0.0 |
| 756 | GO:0021695 | -2.195335 | Cerebellar cortex development (1) | 1.0 |
| 662 | GO:0031648 | -2.195396 | Protein destabilization (1) | 0.0 |
| 727 | GO:0070842 | -2.214919 | Aggresome assembly (1) | 0.0 |
| 4 | GO:2000134 | -2.218448 | Negative regulation of g1/s transition of mitotic cell cycle (1) | 1.0 |
| 500 | GO:0046666 | -2.221412 | Retinal cell programmed cell death (1) | 0.0 |
| 151 | GO:0006959 | -2.228889 | Humoral immune response (1) | 2.0 |
| 216 | GO:0045727 | -2.232845 | Positive regulation of translation (1) | 1.0 |
| 274 | GO:0034983 | -2.241321 | Peptidyl-lysine deacetylation (1) | 0.0 |
| 565 | GO:1902455 | -2.310039 | Negative regulation of stem cell population maintenance (1) | 0.0 |
| 372 | GO:0070584 | -2.320318 | Mitochondrion morphogenesis (1) | 0.0 |
| 343 | GO:1902166 | -2.325709 | Negative regulation of intrinsic apoptotic signaling pathway in response to dna damage by p53 class mediator (1) | 0.0 |
| 512 | GO:0008045 | -2.344717 | Motor neuron axon guidance (1) | 1.0 |
| 769 | GO:0010832 | -2.404325 | Negative regulation of myotube differentiation (1) | 0.0 |
| 905 | GO:0050864 | -2.430784 | Regulation of b cell activation (1) | 2.0 |
| 338 | GO:1900118 | -2.496567 | Negative regulation of execution phase of apoptosis (1) | 0.0 |
| 426 | GO:0090263 | -2.498030 | Positive regulation of canonical wnt signaling pathway (1) | 0.0 |
| 540 | GO:0007617 | -2.520665 | Mating behavior (1) | 1.0 |
| 872 | GO:0002326 | -2.609504 | B cell lineage commitment (1) | 0.0 |
| 680 | GO:0031640 | -2.667289 | Killing of cells of another organism (1) | 1.0 |
| 278 | GO:0016573 | -2.686301 | Histone acetylation (1) | 3.0 |
| 156 | GO:0048536 | -2.714980 | Spleen development (1) | 0.0 |
| 308 | GO:0051926 | -2.798095 | Negative regulation of calcium ion transport (1) | 1.0 |
| 721 | GO:1905710 | -2.815392 | Positive regulation of membrane permeability (1) | 1.0 |
| 665 | GO:0045636 | -2.820949 | Positive regulation of melanocyte differentiation (1) | 0.0 |
| 719 | GO:0043244 | -2.878623 | Regulation of protein-containing complex disassembly (1) | 2.0 |
| 195 | GO:0006360 | -2.938382 | Transcription by rna polymerase i (1) | 2.0 |
| 22 | GO:0007098 | -3.141939 | Centrosome cycle (1) | 2.0 |
| 892 | GO:0070059 | -3.411097 | Intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress (1) | 1.0 |
| 189 | GO:1903800 | -3.468461 | Positive regulation of production of mirnas involved in gene silencing by mirna (1) | 0.0 |
| 844 | GO:0043922 | -3.856003 | Negative regulation by host of viral transcription (1) | 0.0 |
| 452 | GO:0010750 | -3.917943 | Positive regulation of nitric oxide mediated signal transduction (1) | 0.0 |
| 852 | GO:0036289 | -8.673968 | Peptidyl-serine autophosphorylation (1) | 0.0 |
Final model SVM
Once the models have been cross-validated we create the final models using all samples…
GO_terms_auc_svm_final = {}
GO_terms_aupr_svm_final = {}
GO_terms_precision_svm_final = {}
models_svm = {}
# Perform logistics
for goterm in sparseGO_terms:
#print(goterm)
goterm_drugs = slim_matrix.loc[[goterm+"_"+str(1)]].values.flatten()
if sum(goterm_drugs) <= 8:
continue
list_nodes = []
for i in range(1,7):
list_nodes.append(goterm+"_"+str(i))
score = attribution_data_annotated.loc[list_nodes].T
score_mod = score.divide(score.std()).fillna(0)
X_train = score_mod
X_test = score_mod
y_train = goterm_drugs
y_test = goterm_drugs
#gamma = 1/(X_train.shape[1]*X_train.to_numpy().var())
gamma="scale"
C=1
svm_model = svm.SVC(C=C,gamma=gamma, kernel='rbf',
class_weight="balanced",
tol=0.001,
probability=True,
random_state=1234)
# fit the model with data
svm_model.fit(X_train,y_train)
y_pred=svm_model.predict(X_test)
#auc
y_pred_proba = svm_model.predict_proba(X_test)[::,1] # platt values
#y_pred_proba = svm_model.decision_function(X_test)
GO_terms_auc_svm_final[goterm] = metrics.roc_auc_score(y_test, y_pred_proba)
precision, recall, thresholds = metrics.precision_recall_curve(y_test, y_pred_proba)
GO_terms_aupr_svm_final[goterm] = metrics.auc(recall, precision)
GO_terms_precision_svm_final[goterm] = metrics.precision_score(y_test, y_pred)
models_svm[goterm]=svm_modellen(models_svm)939
Final model AUC
GO_terms_auc_svm_df_final = pd.DataFrame(list(GO_terms_auc_svm_final.items()),columns = ['goterm','auc']).set_index("goterm")
GO_terms_auc_svm_df_final = GO_terms_auc_svm_df_final.dropna()
GO_terms_auc_svm_df_final.sort_values(by=["auc"], ascending=False)| auc | |
|---|---|
| goterm | |
| GO:0036289 | 1.000000 |
| GO:0060440 | 0.998540 |
| GO:0043162 | 0.995455 |
| GO:0070059 | 0.994760 |
| GO:0071364 | 0.994109 |
| GO:1901029 | 0.994048 |
| GO:0072384 | 0.993636 |
| GO:0051453 | 0.993393 |
| GO:0001556 | 0.991972 |
| GO:0090201 | 0.991808 |
| GO:0010750 | 0.990909 |
| GO:0016573 | 0.990783 |
| GO:1903800 | 0.990573 |
| GO:1904950 | 0.989945 |
| GO:1902455 | 0.989091 |
| GO:0042149 | 0.987697 |
| GO:0034983 | 0.987273 |
| GO:1990403 | 0.985909 |
| GO:0071353 | 0.985587 |
| GO:0006275 | 0.984226 |
| GO:0010971 | 0.984091 |
| GO:0006869 | 0.983409 |
| GO:0001779 | 0.983182 |
| GO:0051973 | 0.981651 |
| GO:0060749 | 0.980895 |
| GO:0042771 | 0.980633 |
| GO:0072655 | 0.980455 |
| GO:0061734 | 0.980455 |
| GO:0045636 | 0.980178 |
| GO:0045737 | 0.980084 |
| GO:1902236 | 0.979762 |
| GO:0060632 | 0.979545 |
| GO:0016575 | 0.978731 |
| GO:0042659 | 0.977727 |
| GO:0046628 | 0.977376 |
| GO:1902042 | 0.977273 |
| GO:0098780 | 0.975909 |
| GO:0046902 | 0.975849 |
| GO:0051607 | 0.975552 |
| GO:0006401 | 0.974678 |
| GO:0017157 | 0.974040 |
| GO:0032740 | 0.973856 |
| GO:0006270 | 0.973848 |
| GO:0046666 | 0.973570 |
| GO:0008045 | 0.972603 |
| GO:0006303 | 0.972553 |
| GO:0042177 | 0.972431 |
| GO:0060020 | 0.972290 |
| GO:0006360 | 0.972095 |
| GO:2001021 | 0.971520 |
| GO:0042733 | 0.971364 |
| GO:0016572 | 0.971342 |
| GO:0070932 | 0.970909 |
| GO:2001257 | 0.970909 |
| GO:0001782 | 0.970384 |
| GO:0006261 | 0.970112 |
| GO:1905564 | 0.969834 |
| GO:2000757 | 0.969545 |
| GO:0051354 | 0.969091 |
| GO:0072284 | 0.969069 |
| GO:0051926 | 0.968891 |
| GO:0043407 | 0.968585 |
| GO:0034394 | 0.968096 |
| GO:0050870 | 0.967621 |
| GO:0046898 | 0.967143 |
| GO:0031047 | 0.967115 |
| GO:0016925 | 0.966364 |
| GO:0035790 | 0.966361 |
| GO:0006417 | 0.965261 |
| GO:0032469 | 0.965008 |
| GO:0035195 | 0.964816 |
| GO:0021782 | 0.964091 |
| GO:0070584 | 0.963810 |
| GO:0051384 | 0.961083 |
| GO:0002326 | 0.960811 |
| GO:2000773 | 0.960310 |
| GO:0050729 | 0.959779 |
| GO:0046942 | 0.959480 |
| GO:0035249 | 0.959091 |
| GO:0045821 | 0.958904 |
| GO:0099111 | 0.958880 |
| GO:0071670 | 0.958851 |
| GO:0006367 | 0.958333 |
| GO:1905278 | 0.958270 |
| GO:0010559 | 0.957929 |
| GO:0006959 | 0.957854 |
| GO:0018205 | 0.957782 |
| GO:0035860 | 0.957768 |
| GO:0031640 | 0.957381 |
| GO:0007059 | 0.957268 |
| GO:0070373 | 0.956762 |
| GO:0030282 | 0.956762 |
| GO:0001658 | 0.956522 |
| GO:0030890 | 0.956075 |
| GO:0035754 | 0.955757 |
| GO:0010832 | 0.955455 |
| GO:0099173 | 0.955238 |
| GO:0021695 | 0.955238 |
| GO:0045727 | 0.955026 |
| GO:0002862 | 0.954696 |
| GO:0014827 | 0.954432 |
| GO:0016579 | 0.953923 |
| GO:0002718 | 0.953854 |
| GO:0071320 | 0.953746 |
| GO:0051281 | 0.953182 |
| GO:0042552 | 0.953182 |
| GO:0000086 | 0.953095 |
| GO:0032147 | 0.952991 |
| GO:0032436 | 0.952499 |
| GO:0010592 | 0.952273 |
| GO:0006694 | 0.951735 |
| GO:0033141 | 0.951735 |
| GO:0071480 | 0.951429 |
| GO:0006612 | 0.951118 |
| GO:0048011 | 0.950729 |
| GO:1903077 | 0.950714 |
| GO:0033619 | 0.950455 |
| GO:0006352 | 0.950306 |
| GO:0001662 | 0.950221 |
| GO:0010039 | 0.950040 |
| GO:0090314 | 0.949147 |
| GO:0034502 | 0.949074 |
| GO:0014823 | 0.948954 |
| GO:2001240 | 0.948220 |
| GO:0007617 | 0.948182 |
| GO:0032743 | 0.947281 |
| GO:0006310 | 0.947141 |
| GO:0006605 | 0.946678 |
| GO:0006975 | 0.946204 |
| GO:2000739 | 0.946101 |
| GO:1902459 | 0.945909 |
| GO:0007626 | 0.945701 |
| GO:0023019 | 0.945116 |
| GO:0003376 | 0.944700 |
| GO:0006576 | 0.944346 |
| GO:0038007 | 0.943690 |
| GO:0050728 | 0.943637 |
| GO:0032922 | 0.942661 |
| GO:0045740 | 0.942465 |
| GO:1900118 | 0.942381 |
| GO:0010952 | 0.942143 |
| GO:1905710 | 0.942143 |
| GO:1902166 | 0.942128 |
| GO:0008637 | 0.941950 |
| GO:2000010 | 0.941865 |
| GO:0055118 | 0.941679 |
| GO:0000423 | 0.941364 |
| GO:0043154 | 0.941156 |
| GO:0048701 | 0.940775 |
| GO:0008210 | 0.940749 |
| GO:1900272 | 0.940171 |
| GO:0060997 | 0.939809 |
| GO:0007263 | 0.939545 |
| GO:2000379 | 0.939167 |
| GO:1900020 | 0.939091 |
| GO:0050896 | 0.938915 |
| GO:0016485 | 0.938636 |
| GO:0043966 | 0.938376 |
| GO:0002437 | 0.938295 |
| GO:2000300 | 0.937318 |
| GO:0140013 | 0.937095 |
| GO:0034767 | 0.936758 |
| GO:0031648 | 0.936624 |
| GO:0007026 | 0.936364 |
| GO:0032024 | 0.936149 |
| GO:0030193 | 0.936040 |
| GO:0010212 | 0.935098 |
| GO:0006457 | 0.934641 |
| GO:0032729 | 0.934420 |
| GO:0030593 | 0.934413 |
| GO:0010575 | 0.934272 |
| GO:0008064 | 0.933643 |
| GO:0008286 | 0.932331 |
| GO:0001818 | 0.932128 |
| GO:0030513 | 0.931404 |
| GO:0060766 | 0.931364 |
| GO:0006396 | 0.931346 |
| GO:0006919 | 0.931342 |
| GO:0038096 | 0.930886 |
| GO:0001553 | 0.930810 |
| GO:0045580 | 0.930407 |
| GO:0046326 | 0.930406 |
| GO:0035025 | 0.930294 |
| GO:1903146 | 0.929091 |
| GO:0060444 | 0.929091 |
| GO:0006412 | 0.928571 |
| GO:0048536 | 0.928290 |
| GO:0002819 | 0.927685 |
| GO:0048704 | 0.927370 |
| GO:0051054 | 0.927333 |
| GO:0090184 | 0.927099 |
| GO:1900006 | 0.926941 |
| GO:2000134 | 0.926917 |
| GO:0046889 | 0.926822 |
| GO:0043123 | 0.926512 |
| GO:0070842 | 0.926364 |
| GO:0046329 | 0.926364 |
| GO:0006898 | 0.925891 |
| GO:0006368 | 0.925841 |
| GO:1905897 | 0.925743 |
| GO:0030048 | 0.925591 |
| GO:0042180 | 0.925076 |
| GO:0035909 | 0.924883 |
| GO:0051209 | 0.924065 |
| GO:0030308 | 0.923951 |
| GO:0043170 | 0.923707 |
| GO:0035726 | 0.922783 |
| GO:0031663 | 0.922727 |
| GO:0000209 | 0.922119 |
| GO:0009165 | 0.921544 |
| GO:0002720 | 0.921427 |
| GO:0006096 | 0.921292 |
| GO:1902036 | 0.921254 |
| GO:0071549 | 0.921066 |
| GO:0007528 | 0.920950 |
| GO:0090090 | 0.920930 |
| GO:0042472 | 0.920455 |
| GO:0031056 | 0.920429 |
| GO:0050864 | 0.920262 |
| GO:0060789 | 0.920000 |
| GO:0007389 | 0.919762 |
| GO:0048743 | 0.919572 |
| GO:0030705 | 0.919116 |
| GO:0060179 | 0.919091 |
| GO:0045739 | 0.918823 |
| GO:0043627 | 0.917977 |
| GO:0040018 | 0.917659 |
| GO:2001243 | 0.917078 |
| GO:0090037 | 0.917056 |
| GO:0040016 | 0.915987 |
| GO:0043552 | 0.915951 |
| GO:0001666 | 0.915013 |
| GO:0010508 | 0.914755 |
| GO:0033690 | 0.914545 |
| GO:0098586 | 0.914419 |
| GO:0043922 | 0.914091 |
| GO:0035994 | 0.914021 |
| GO:0031398 | 0.913694 |
| GO:0042093 | 0.913524 |
| GO:0032410 | 0.913182 |
| GO:1901224 | 0.913182 |
| GO:0006839 | 0.913167 |
| GO:0045907 | 0.912844 |
| GO:2000278 | 0.912619 |
| GO:2001236 | 0.912563 |
| GO:0048170 | 0.912474 |
| GO:0071839 | 0.912217 |
| GO:0031507 | 0.911552 |
| GO:0060391 | 0.911011 |
| GO:0032148 | 0.910451 |
| GO:0070102 | 0.910000 |
| GO:0030878 | 0.909762 |
| GO:0035162 | 0.909463 |
| GO:0051225 | 0.909314 |
| GO:0002931 | 0.909064 |
| GO:0007411 | 0.908683 |
| GO:0008625 | 0.908500 |
| GO:0035788 | 0.908313 |
| GO:0010921 | 0.907360 |
| GO:0048266 | 0.906977 |
| GO:0010977 | 0.906667 |
| GO:0050910 | 0.906656 |
| GO:0045732 | 0.906062 |
| GO:0046620 | 0.905714 |
| GO:0035855 | 0.905551 |
| GO:0030316 | 0.905551 |
| GO:0006469 | 0.905340 |
| GO:0090263 | 0.905136 |
| GO:0021953 | 0.904874 |
| GO:0060312 | 0.904790 |
| GO:0006260 | 0.904703 |
| GO:0030521 | 0.904434 |
| GO:0008016 | 0.904091 |
| GO:0010727 | 0.904091 |
| GO:0030509 | 0.904035 |
| GO:0007498 | 0.903914 |
| GO:0050769 | 0.903592 |
| GO:0050792 | 0.903414 |
| GO:0009582 | 0.903167 |
| GO:0007098 | 0.902745 |
| GO:0002821 | 0.902464 |
| GO:0071276 | 0.902162 |
| GO:0007286 | 0.901132 |
| GO:0045088 | 0.900952 |
| GO:0055003 | 0.900943 |
| GO:0035767 | 0.900748 |
| GO:0045987 | 0.900474 |
| GO:0061029 | 0.900474 |
| GO:0033327 | 0.900465 |
| GO:0000422 | 0.900374 |
| GO:0010976 | 0.900117 |
| GO:0008354 | 0.899895 |
| GO:0070528 | 0.899726 |
| GO:0006807 | 0.899601 |
| GO:0045833 | 0.899128 |
| GO:1905065 | 0.898923 |
| GO:0007018 | 0.898915 |
| GO:0007422 | 0.898647 |
| GO:0048484 | 0.898636 |
| GO:0032467 | 0.898182 |
| GO:0050795 | 0.897909 |
| GO:0030539 | 0.897909 |
| GO:0048538 | 0.897833 |
| GO:0032355 | 0.897646 |
| GO:0007416 | 0.897554 |
| GO:0021575 | 0.897509 |
| GO:0060348 | 0.897410 |
| GO:0001569 | 0.897282 |
| GO:0060384 | 0.897171 |
| GO:0031069 | 0.897099 |
| GO:0050918 | 0.897059 |
| GO:0035584 | 0.896905 |
| GO:0051046 | 0.896369 |
| GO:0043129 | 0.896233 |
| GO:0001843 | 0.896024 |
| GO:0046330 | 0.895444 |
| GO:0007030 | 0.895429 |
| GO:0048873 | 0.895092 |
| GO:0000724 | 0.894922 |
| GO:0007202 | 0.894511 |
| GO:1903053 | 0.894419 |
| GO:0003338 | 0.894238 |
| GO:1901990 | 0.894150 |
| GO:0060644 | 0.893917 |
| GO:0043161 | 0.893782 |
| GO:0030838 | 0.892727 |
| GO:0001946 | 0.892571 |
| GO:0072210 | 0.892039 |
| GO:0030101 | 0.892003 |
| GO:0050731 | 0.892003 |
| GO:0010613 | 0.891865 |
| GO:0030325 | 0.891865 |
| GO:0048714 | 0.891783 |
| GO:0048008 | 0.891667 |
| GO:0001823 | 0.890989 |
| GO:0016239 | 0.890496 |
| GO:0030216 | 0.890460 |
| GO:0071300 | 0.890341 |
| GO:0032008 | 0.889952 |
| GO:0061045 | 0.889881 |
| GO:0051894 | 0.889619 |
| GO:0030010 | 0.889612 |
| GO:0031016 | 0.889533 |
| GO:0001942 | 0.889526 |
| GO:1902533 | 0.889155 |
| GO:0016358 | 0.888660 |
| GO:0001501 | 0.888280 |
| GO:0051092 | 0.888251 |
| GO:0016601 | 0.887883 |
| GO:0097067 | 0.887324 |
| GO:0009306 | 0.887019 |
| GO:0048167 | 0.886555 |
| GO:0050921 | 0.886315 |
| GO:1990384 | 0.886268 |
| GO:0046883 | 0.886202 |
| GO:0007519 | 0.886154 |
| GO:0043270 | 0.885881 |
| GO:0003007 | 0.885720 |
| GO:0071900 | 0.885420 |
| GO:0007585 | 0.885391 |
| GO:2001214 | 0.885258 |
| GO:0071456 | 0.884685 |
| GO:0016567 | 0.884594 |
| GO:0060740 | 0.882856 |
| GO:0035094 | 0.882732 |
| GO:0072073 | 0.882732 |
| GO:0060612 | 0.881602 |
| GO:0060325 | 0.881498 |
| GO:0045668 | 0.881347 |
| GO:0042531 | 0.881332 |
| GO:0010038 | 0.881167 |
| GO:0071333 | 0.880972 |
| GO:0006939 | 0.880907 |
| GO:0090141 | 0.880907 |
| GO:0046718 | 0.880697 |
| GO:0051770 | 0.880461 |
| GO:0033627 | 0.880455 |
| GO:0048149 | 0.880352 |
| GO:0002685 | 0.880291 |
| GO:0043029 | 0.880195 |
| GO:0038033 | 0.879699 |
| GO:0055119 | 0.879336 |
| GO:0003300 | 0.878843 |
| GO:0005984 | 0.878788 |
| GO:0002218 | 0.878773 |
| GO:0072239 | 0.878669 |
| GO:0031103 | 0.878667 |
| GO:0048557 | 0.878638 |
| GO:1901987 | 0.878627 |
| GO:0060048 | 0.877703 |
| GO:0045637 | 0.877659 |
| GO:2001234 | 0.877406 |
| GO:0038083 | 0.876762 |
| GO:0071277 | 0.876323 |
| GO:0048839 | 0.876278 |
| GO:0000723 | 0.875714 |
| GO:0060627 | 0.875648 |
| GO:0035022 | 0.874811 |
| GO:0007435 | 0.874669 |
| GO:2001241 | 0.874309 |
| GO:0002062 | 0.874091 |
| GO:0035234 | 0.873792 |
| GO:0034976 | 0.873754 |
| GO:0007584 | 0.872411 |
| GO:0002318 | 0.872408 |
| GO:0001975 | 0.872354 |
| GO:0071230 | 0.871837 |
| GO:0034446 | 0.871788 |
| GO:0070933 | 0.871364 |
| GO:0030072 | 0.871331 |
| GO:0071897 | 0.871171 |
| GO:0035733 | 0.870478 |
| GO:0032967 | 0.870403 |
| GO:0048675 | 0.870071 |
| GO:0060571 | 0.870035 |
| GO:0050920 | 0.869917 |
| GO:0050678 | 0.869106 |
| GO:0034405 | 0.869048 |
| GO:0051150 | 0.868932 |
| GO:0001934 | 0.868720 |
| GO:0010507 | 0.868700 |
| GO:1904707 | 0.868636 |
| GO:0050821 | 0.868325 |
| GO:0006811 | 0.868262 |
| GO:0070588 | 0.868155 |
| GO:0014911 | 0.867596 |
| GO:0090280 | 0.867440 |
| GO:0008630 | 0.867386 |
| GO:1901796 | 0.867386 |
| GO:0051056 | 0.867368 |
| GO:0051321 | 0.865996 |
| GO:0051051 | 0.865833 |
| GO:0051902 | 0.865573 |
| GO:0097009 | 0.865089 |
| GO:0060271 | 0.865061 |
| GO:0045930 | 0.864995 |
| GO:0035304 | 0.864977 |
| GO:0051899 | 0.864866 |
| GO:0033028 | 0.864808 |
| GO:0018108 | 0.864767 |
| GO:1900087 | 0.864434 |
| GO:0010467 | 0.863952 |
| GO:0035019 | 0.863557 |
| GO:0006687 | 0.863557 |
| GO:0001824 | 0.863532 |
| GO:0033689 | 0.863522 |
| GO:0071392 | 0.863443 |
| GO:0035264 | 0.863252 |
| GO:0046632 | 0.862800 |
| GO:0034605 | 0.862619 |
| GO:0032091 | 0.862599 |
| GO:0072659 | 0.862358 |
| GO:0051901 | 0.861670 |
| GO:0006357 | 0.861504 |
| GO:0042475 | 0.861448 |
| GO:0045747 | 0.861374 |
| GO:0072006 | 0.860598 |
| GO:0042220 | 0.860483 |
| GO:0006937 | 0.860353 |
| GO:0006511 | 0.860111 |
| GO:0010718 | 0.859229 |
| GO:0035924 | 0.859169 |
| GO:0090398 | 0.859050 |
| GO:0031532 | 0.858981 |
| GO:1904062 | 0.858745 |
| GO:2000251 | 0.858605 |
| GO:0014068 | 0.858156 |
| GO:0048146 | 0.858102 |
| GO:0051090 | 0.857756 |
| GO:0034765 | 0.857317 |
| GO:0007229 | 0.856812 |
| GO:0007158 | 0.856712 |
| GO:1901031 | 0.856712 |
| GO:0061351 | 0.856372 |
| GO:1904019 | 0.856183 |
| GO:0048812 | 0.856107 |
| GO:0060437 | 0.855565 |
| GO:0034766 | 0.854758 |
| GO:0033143 | 0.854574 |
| GO:0007269 | 0.854497 |
| GO:0032516 | 0.854484 |
| GO:0036120 | 0.854433 |
| GO:0090068 | 0.853947 |
| GO:0046854 | 0.853881 |
| GO:0010811 | 0.853842 |
| GO:0060976 | 0.853774 |
| GO:0060045 | 0.853680 |
| GO:0021549 | 0.853311 |
| GO:0043534 | 0.853142 |
| GO:0038084 | 0.853135 |
| GO:0046427 | 0.852947 |
| GO:0030324 | 0.852866 |
| GO:0048010 | 0.852488 |
| GO:0097193 | 0.852297 |
| GO:0048286 | 0.852143 |
| GO:0006468 | 0.851852 |
| GO:0060326 | 0.851772 |
| GO:0034097 | 0.851678 |
| GO:0016071 | 0.851667 |
| GO:0036324 | 0.851085 |
| GO:1903010 | 0.851085 |
| GO:0002327 | 0.850962 |
| GO:0001570 | 0.850955 |
| GO:0043536 | 0.850601 |
| GO:0043406 | 0.850494 |
| GO:0045347 | 0.850455 |
| GO:0001701 | 0.850196 |
| GO:0019222 | 0.849913 |
| GO:0051403 | 0.849741 |
| GO:0097021 | 0.849170 |
| GO:0043467 | 0.848706 |
| GO:0045766 | 0.848621 |
| GO:0060562 | 0.848060 |
| GO:0030001 | 0.847486 |
| GO:0006810 | 0.847446 |
| GO:0031667 | 0.847070 |
| GO:0048565 | 0.846000 |
| GO:0019827 | 0.845649 |
| GO:0007565 | 0.845356 |
| GO:0009966 | 0.844893 |
| GO:0055085 | 0.844768 |
| GO:0043114 | 0.844749 |
| GO:0002548 | 0.844626 |
| GO:2000377 | 0.844341 |
| GO:0030198 | 0.844187 |
| GO:0032386 | 0.844167 |
| GO:0031929 | 0.844150 |
| GO:0035306 | 0.843956 |
| GO:0006897 | 0.843955 |
| GO:0051301 | 0.843815 |
| GO:0001656 | 0.843809 |
| GO:0042060 | 0.843773 |
| GO:0031109 | 0.843563 |
| GO:0000122 | 0.843521 |
| GO:0043124 | 0.843017 |
| GO:0001837 | 0.842638 |
| GO:1902275 | 0.841719 |
| GO:0051261 | 0.841719 |
| GO:0051924 | 0.841520 |
| GO:0002250 | 0.841465 |
| GO:0030336 | 0.841059 |
| GO:0046631 | 0.840909 |
| GO:0016055 | 0.840841 |
| GO:0033077 | 0.840735 |
| GO:0048741 | 0.840370 |
| GO:0007266 | 0.839667 |
| GO:0001938 | 0.838948 |
| GO:0043586 | 0.838898 |
| GO:0008277 | 0.837920 |
| GO:0043303 | 0.837858 |
| GO:0070662 | 0.837526 |
| GO:0060374 | 0.836916 |
| GO:0045087 | 0.836889 |
| GO:0034220 | 0.836107 |
| GO:0032388 | 0.835532 |
| GO:0048568 | 0.835305 |
| GO:0050866 | 0.835227 |
| GO:0009058 | 0.834946 |
| GO:1902074 | 0.834912 |
| GO:0043244 | 0.834906 |
| GO:0008542 | 0.834749 |
| GO:0045055 | 0.834433 |
| GO:0045444 | 0.834286 |
| GO:0046578 | 0.834019 |
| GO:0046777 | 0.833773 |
| GO:0001889 | 0.833595 |
| GO:0008584 | 0.833556 |
| GO:0045840 | 0.833536 |
| GO:0002366 | 0.833530 |
| GO:0007049 | 0.833424 |
| GO:0046474 | 0.833392 |
| GO:0019233 | 0.833182 |
| GO:0000165 | 0.832917 |
| GO:0051258 | 0.832656 |
| GO:0032956 | 0.832450 |
| GO:0022612 | 0.832326 |
| GO:0051050 | 0.832281 |
| GO:0043392 | 0.831905 |
| GO:0031274 | 0.831814 |
| GO:0051702 | 0.831506 |
| GO:0010564 | 0.831039 |
| GO:0031099 | 0.830615 |
| GO:1905563 | 0.830607 |
| GO:0030318 | 0.830136 |
| GO:0048598 | 0.829861 |
| GO:0007165 | 0.829719 |
| GO:1901988 | 0.829474 |
| GO:0007186 | 0.829429 |
| GO:0033157 | 0.829023 |
| GO:0019221 | 0.829000 |
| GO:0000278 | 0.828800 |
| GO:0042310 | 0.828784 |
| GO:1901300 | 0.828616 |
| GO:0006909 | 0.828497 |
| GO:0030154 | 0.828332 |
| GO:0002573 | 0.827001 |
| GO:0045429 | 0.826889 |
| GO:0051223 | 0.826823 |
| GO:0016570 | 0.826822 |
| GO:0030163 | 0.826442 |
| GO:0009791 | 0.826355 |
| GO:0090630 | 0.826069 |
| GO:0032409 | 0.825426 |
| GO:0048477 | 0.824868 |
| GO:0034644 | 0.824849 |
| GO:0007346 | 0.824841 |
| GO:0046651 | 0.824539 |
| GO:0051171 | 0.823977 |
| GO:0000302 | 0.823816 |
| GO:0048608 | 0.823637 |
| GO:0032940 | 0.823481 |
| GO:0008610 | 0.823469 |
| GO:0010628 | 0.823151 |
| GO:1903078 | 0.822244 |
| GO:0016032 | 0.821730 |
| GO:0009888 | 0.821458 |
| GO:0016042 | 0.821320 |
| GO:0007259 | 0.820971 |
| GO:0008544 | 0.820813 |
| GO:0000077 | 0.820719 |
| GO:0021766 | 0.820586 |
| GO:0001817 | 0.819733 |
| GO:0001932 | 0.819683 |
| GO:0002053 | 0.819493 |
| GO:0072593 | 0.819390 |
| GO:0009887 | 0.819242 |
| GO:0006753 | 0.818971 |
| GO:0071383 | 0.818684 |
| GO:0007015 | 0.818627 |
| GO:0001819 | 0.818452 |
| GO:0007275 | 0.818394 |
| GO:1903829 | 0.818083 |
| GO:0002244 | 0.818060 |
| GO:0051898 | 0.817795 |
| GO:0009410 | 0.817265 |
| GO:0030335 | 0.817025 |
| GO:0061024 | 0.816492 |
| GO:0007173 | 0.816349 |
| GO:0050900 | 0.816242 |
| GO:0060395 | 0.815909 |
| GO:0009755 | 0.815667 |
| GO:0045860 | 0.815613 |
| GO:0050872 | 0.815367 |
| GO:0007612 | 0.814548 |
| GO:0000082 | 0.814519 |
| GO:0050852 | 0.814267 |
| GO:0043408 | 0.813977 |
| GO:0002009 | 0.813874 |
| GO:0019752 | 0.813530 |
| GO:0001822 | 0.813506 |
| GO:0007179 | 0.813500 |
| GO:0051049 | 0.813439 |
| GO:0010033 | 0.813421 |
| GO:1901135 | 0.813379 |
| GO:1900180 | 0.813213 |
| GO:0033554 | 0.813172 |
| GO:0007204 | 0.813136 |
| GO:0044770 | 0.812960 |
| GO:0001755 | 0.812831 |
| GO:0001541 | 0.812614 |
| GO:0006470 | 0.811795 |
| GO:0009743 | 0.811594 |
| GO:0033993 | 0.811585 |
| GO:0035265 | 0.811041 |
| GO:0051496 | 0.811040 |
| GO:0007162 | 0.810927 |
| GO:0030218 | 0.809955 |
| GO:0006139 | 0.809816 |
| GO:0070374 | 0.808642 |
| GO:0006298 | 0.808612 |
| GO:0009056 | 0.808581 |
| GO:0070507 | 0.808431 |
| GO:0071363 | 0.808295 |
| GO:0050680 | 0.808234 |
| GO:0007169 | 0.807939 |
| GO:0001894 | 0.807870 |
| GO:0000902 | 0.806862 |
| GO:0009617 | 0.806711 |
| GO:1902904 | 0.806512 |
| GO:0030097 | 0.806125 |
| GO:0007399 | 0.805949 |
| GO:0050853 | 0.805230 |
| GO:0051726 | 0.804914 |
| GO:0008360 | 0.804780 |
| GO:0050863 | 0.804772 |
| GO:0010629 | 0.804702 |
| GO:0032880 | 0.804305 |
| GO:0021795 | 0.804198 |
| GO:0046488 | 0.804184 |
| GO:0031032 | 0.804004 |
| GO:0045595 | 0.803077 |
| GO:0006936 | 0.802344 |
| GO:0045793 | 0.802149 |
| GO:0071222 | 0.801980 |
| GO:0051897 | 0.801416 |
| GO:0006606 | 0.800953 |
| GO:0006886 | 0.800872 |
| GO:0030307 | 0.800490 |
| GO:0048738 | 0.800331 |
| GO:0010821 | 0.800220 |
| GO:0051247 | 0.800154 |
| GO:0042752 | 0.800120 |
| GO:0032835 | 0.800025 |
| GO:0033138 | 0.799982 |
| GO:1903578 | 0.799701 |
| GO:0050673 | 0.798946 |
| GO:0006997 | 0.798672 |
| GO:0060341 | 0.798662 |
| GO:0006281 | 0.798556 |
| GO:0042391 | 0.798475 |
| GO:0050808 | 0.797394 |
| GO:0007267 | 0.797360 |
| GO:0050865 | 0.797107 |
| GO:0018105 | 0.797070 |
| GO:0060560 | 0.796569 |
| GO:0071478 | 0.796131 |
| GO:0018107 | 0.796045 |
| GO:0019216 | 0.795977 |
| GO:0023061 | 0.795969 |
| GO:0036473 | 0.795897 |
| GO:0051147 | 0.795455 |
| GO:0006996 | 0.794900 |
| GO:0030217 | 0.794761 |
| GO:0070527 | 0.794579 |
| GO:0050804 | 0.793936 |
| GO:0060021 | 0.793808 |
| GO:0045321 | 0.793792 |
| GO:0046034 | 0.792891 |
| GO:1904646 | 0.792812 |
| GO:0030182 | 0.792624 |
| GO:0002764 | 0.790893 |
| GO:0007596 | 0.790844 |
| GO:0043542 | 0.790474 |
| GO:0006355 | 0.790400 |
| GO:0010638 | 0.790227 |
| GO:0042110 | 0.789916 |
| GO:2000811 | 0.789519 |
| GO:0045785 | 0.789271 |
| GO:0001952 | 0.789204 |
| GO:0048709 | 0.787833 |
| GO:0016192 | 0.787802 |
| GO:0002320 | 0.787705 |
| GO:0045944 | 0.787650 |
| GO:0035051 | 0.787216 |
| GO:0070663 | 0.786907 |
| GO:0046486 | 0.786765 |
| GO:0006914 | 0.786701 |
| GO:0071407 | 0.786480 |
| GO:0048468 | 0.786471 |
| GO:0043065 | 0.786229 |
| GO:1902532 | 0.786009 |
| GO:0033044 | 0.785934 |
| GO:0031333 | 0.785379 |
| GO:0071417 | 0.785307 |
| GO:0016241 | 0.785238 |
| GO:0007268 | 0.785105 |
| GO:0007010 | 0.785047 |
| GO:0002443 | 0.783904 |
| GO:2000270 | 0.783308 |
| GO:0001764 | 0.782709 |
| GO:0051174 | 0.781935 |
| GO:0034329 | 0.781439 |
| GO:0043549 | 0.781269 |
| GO:0010595 | 0.781136 |
| GO:2001020 | 0.780899 |
| GO:0050776 | 0.780250 |
| GO:0007159 | 0.780220 |
| GO:0048041 | 0.780105 |
| GO:0016236 | 0.779569 |
| GO:0048638 | 0.778556 |
| GO:0042551 | 0.778521 |
| GO:0007517 | 0.778474 |
| GO:0032869 | 0.777921 |
| GO:0051649 | 0.777222 |
| GO:0009725 | 0.777056 |
| GO:0030855 | 0.776398 |
| GO:0002040 | 0.776347 |
| GO:0071310 | 0.775759 |
| GO:0042063 | 0.775499 |
| GO:0009266 | 0.775262 |
| GO:0048469 | 0.774721 |
| GO:0042307 | 0.774054 |
| GO:0032879 | 0.772742 |
| GO:0002376 | 0.772696 |
| GO:0055082 | 0.772549 |
| GO:0016070 | 0.772150 |
| GO:0060840 | 0.771853 |
| GO:0010632 | 0.771656 |
| GO:0007219 | 0.771429 |
| GO:0051341 | 0.770833 |
| GO:0060416 | 0.770267 |
| GO:0090050 | 0.770256 |
| GO:0002274 | 0.770035 |
| GO:0009968 | 0.768538 |
| GO:0009416 | 0.768293 |
| GO:0009653 | 0.767978 |
| GO:0030183 | 0.767941 |
| GO:0007507 | 0.766819 |
| GO:0007283 | 0.766625 |
| GO:0048589 | 0.766590 |
| GO:0050790 | 0.766284 |
| GO:0065003 | 0.765562 |
| GO:0030032 | 0.765559 |
| GO:0048103 | 0.765258 |
| GO:0006954 | 0.764565 |
| GO:0048878 | 0.764329 |
| GO:0007420 | 0.764092 |
| GO:0030168 | 0.762921 |
| GO:0006629 | 0.761422 |
| GO:0006644 | 0.760398 |
| GO:0001525 | 0.760172 |
| GO:0120035 | 0.759979 |
| GO:0034103 | 0.759958 |
| GO:0014070 | 0.759563 |
| GO:0044255 | 0.758471 |
| GO:0051098 | 0.758377 |
| GO:0051641 | 0.757853 |
| GO:0034599 | 0.756607 |
| GO:0043473 | 0.756079 |
| GO:0036092 | 0.755500 |
| GO:0048863 | 0.755435 |
| GO:2000352 | 0.754950 |
| GO:0030162 | 0.754327 |
| GO:0042325 | 0.754119 |
| GO:0008202 | 0.754059 |
| GO:0033628 | 0.753988 |
| GO:0051146 | 0.753713 |
| GO:0010243 | 0.753077 |
| GO:0043524 | 0.752485 |
| GO:0003014 | 0.752381 |
| GO:0002684 | 0.752271 |
| GO:0001763 | 0.751863 |
| GO:0051145 | 0.751530 |
| GO:0045596 | 0.750733 |
| GO:0000226 | 0.750411 |
| GO:0031175 | 0.749603 |
| GO:0007155 | 0.749178 |
| GO:0002064 | 0.748667 |
| GO:0045597 | 0.748593 |
| GO:0040008 | 0.748313 |
| GO:0060485 | 0.746706 |
| GO:0006508 | 0.746456 |
| GO:0097191 | 0.746084 |
| GO:0016477 | 0.745481 |
| GO:0005975 | 0.745307 |
| GO:0043066 | 0.745136 |
| GO:0050890 | 0.744664 |
| GO:0007265 | 0.744598 |
| GO:0032092 | 0.743751 |
| GO:0051017 | 0.743352 |
| GO:0007005 | 0.742652 |
| GO:0043434 | 0.742583 |
| GO:0003158 | 0.742221 |
| GO:0042113 | 0.742005 |
| GO:0120162 | 0.741508 |
| GO:0051881 | 0.740169 |
| GO:0030522 | 0.740000 |
| GO:0007160 | 0.739980 |
| GO:0048511 | 0.737766 |
| GO:0044281 | 0.736585 |
| GO:0007568 | 0.736500 |
| GO:0007610 | 0.735604 |
| GO:0035556 | 0.734890 |
| GO:0048017 | 0.734281 |
| GO:0006325 | 0.734050 |
| GO:0006915 | 0.733434 |
| GO:0008284 | 0.731624 |
| GO:0045165 | 0.731183 |
| GO:0002682 | 0.730932 |
| GO:0022414 | 0.730731 |
| GO:0097190 | 0.730349 |
| GO:0051494 | 0.729426 |
| GO:0051128 | 0.728822 |
| GO:0043254 | 0.728497 |
| GO:0098609 | 0.727096 |
| GO:0006338 | 0.726229 |
| GO:0007423 | 0.725167 |
| GO:0001649 | 0.721908 |
| GO:0048661 | 0.721041 |
| GO:0010941 | 0.719507 |
| GO:1900407 | 0.716777 |
| GO:0007166 | 0.714859 |
| GO:0002683 | 0.708778 |
| GO:1902903 | 0.708773 |
| GO:0008285 | 0.705969 |
| GO:0030900 | 0.704312 |
| GO:0034504 | 0.704188 |
| GO:0033365 | 0.703782 |
| GO:0070997 | 0.703482 |
| GO:0033043 | 0.697664 |
| GO:0051249 | 0.687925 |
| GO:0008104 | 0.675076 |
| GO:0033002 | 0.627125 |
| GO:0042593 | 0.364293 |
| GO:0071887 | 0.359703 |
| GO:0044262 | 0.349744 |
| GO:0051640 | 0.348052 |
| GO:0051000 | 0.299934 |
| GO:0050778 | 0.290903 |
| GO:0007156 | 0.278928 |
| GO:0008361 | 0.265902 |
| GO:0070301 | 0.262988 |
| GO:0022407 | 0.258553 |
| GO:0015031 | 0.257560 |
| GO:0043525 | 0.248848 |
| GO:0051353 | 0.242570 |
| GO:0043086 | 0.239824 |
| GO:0045471 | 0.229314 |
| GO:0051497 | 0.208992 |
| GO:0031529 | 0.208648 |
| GO:0099504 | 0.206822 |
| GO:0043547 | 0.194217 |
| GO:1904659 | 0.191457 |
| GO:0031334 | 0.184335 |
| GO:0046677 | 0.183479 |
| GO:0015980 | 0.180476 |
| GO:0060291 | 0.175234 |
| GO:0009259 | 0.166290 |
| GO:0060173 | 0.162212 |
| GO:0042632 | 0.145299 |
| GO:0046890 | 0.144186 |
| GO:0032760 | 0.142722 |
| GO:0051302 | 0.135000 |
| GO:0031295 | 0.134696 |
| GO:0019318 | 0.123006 |
| GO:0010951 | 0.120040 |
| GO:0021987 | 0.119137 |
| GO:0006163 | 0.118024 |
| GO:0030041 | 0.107955 |
| GO:0001892 | 0.106324 |
| GO:0030512 | 0.105991 |
| GO:0060079 | 0.105991 |
| GO:0050770 | 0.098547 |
| GO:0051928 | 0.097553 |
| GO:0031397 | 0.094042 |
| GO:0060041 | 0.082956 |
| GO:0051047 | 0.076258 |
| GO:0019722 | 0.041730 |
| GO:0090042 | 0.035699 |
sns.set(rc={'figure.figsize':(6,4)})
perc = str(round((100*len(GO_terms_auc_svm_df_final[GO_terms_auc_svm_df_final["auc"]>0.7])/len(GO_terms_auc_svm_df_final)),2))+"%"
N, bins, patches = plt.hist(GO_terms_auc_svm_df_final, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.7:
patches[i].set_facecolor(CB_color_cycle[2])
plt.xlabel("AUC (logistic 1)", fontsize=16)
plt.title(perc, fontsize=16)
# con el que mejor funciona es con la suma normal del attribution Text(0.5, 1.0, '94.68%')

Final model AUPR
GO_terms_aupr_svm_df_final = pd.DataFrame(list(GO_terms_aupr_svm_final.items()),columns = ['goterm','aupr']).set_index("goterm")
GO_terms_aupr_svm_df_final = GO_terms_aupr_svm_df_final.dropna()
GO_terms_aupr_svm_df_final.sort_values(by=["aupr"], ascending=False).head()| aupr | |
|---|---|
| goterm | |
| GO:0036289 | 1.000000 |
| GO:0050896 | 0.995438 |
| GO:0043170 | 0.989680 |
| GO:0006807 | 0.987396 |
| GO:0060440 | 0.978213 |
# TENGO PROBLEMA CON EL RECALL
sns.set(rc={'figure.figsize':(5,3)})
perc = str(round((100*len(GO_terms_aupr_svm_df_final[GO_terms_aupr_svm_df_final["aupr"]>0.7])/len(GO_terms_aupr_svm_df_final)),2))+"%"
N, bins, patches = plt.hist(GO_terms_aupr_svm_df_final, color=CB_color_cycle[6],bins=50, linewidth=0.1)
for i in range(0,len(bins)-1):
if bins[i]>0.7:
patches[i].set_facecolor(CB_color_cycle[3])
plt.xlabel("AUPR", fontsize=16)
plt.title(perc, fontsize=16)Text(0.5, 1.0, '20.02%')

Predict for a new drug
Make predictions
unknown = list(set(attribution_data_all.columns)-set(attribution_data_annotated.columns))Get the probabilities for all unknown drugs
predictions = {}
distances = {}
probabilities_unknown = pd.DataFrame()
preds_unknown = pd.DataFrame()
for drug in unknown:
probabilities = {}
for goterm in models_svm.keys():
list_nodes = list(models_svm[goterm].feature_names_in_) # Extract the feature names from the model (those are the attributions we need)
score = attribution_data_all.loc[list_nodes][drug].to_frame().T
score_mod = score.divide(attribution_data_annotated.loc[list_nodes].T.std()).fillna(0) #divide by std of each neuron, only use drugs that trained the models
predictions[goterm]=models_svm[goterm].predict(score_mod)
probabilities[goterm] = models_svm[goterm].predict_proba(score_mod)[::,1] # platt values
# distances[goterm] = models_svm[goterm].decision_function(score_mod)
drug_probs = pd.DataFrame.from_dict(probabilities).T
drug_probs.columns = [drug]
drug_preds = pd.DataFrame.from_dict(predictions).T
drug_preds.columns = [drug]
probabilities_unknown = pd.concat([probabilities_unknown,drug_probs], axis=1)
preds_unknown = pd.concat([preds_unknown,drug_preds], axis=1)
print(drug)brd-k19103580-001-01-2
nvp-bhg712
wh-4-023
pd173074
cbpnzqvsjqdfbe-rerlvdevsa-n
n-(2,5-dimethoxyphenyl)sulfonyl-n-(4-methoxyphenyl)benzamide
brd-k33514849-001-01-9
chembl3182697
dfsdbfjuwanyes-ubwkhrtasa-n
nvp-adw742
sb 225002
schembl10436373
stf-62247
zm-447439
gsk269962a
schembl12469828
schembl2139153
azd7545
mira-1
wee1 inhibitor
achp
chembl2203525
brd-k49290616-001-01-9
nvp-231
pha-665752
jq1
n-[(2r,3s)-2-[[cyclopropylmethyl(methyl)amino]methyl]-5-[(2r)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-3,4-dihydro-2h-1,5-benzoxazocin-8-yl]-1-methyl-4-imidazolesulfonamide
otkwubxkthwzke-fuopvmcbsa-n
bms-345541;cc1=cc2=c(c=c1)n=c(c3=nc=c(n23)c)nccn.cl
cid5951923
ml-030
cct036477
pluripotin
nutlin-3a
skepinone-l
gsk-j4
(-)-rapamycin
hhdwuyjenprcsp-uttpphfysa-n
r406 (free base)
n-[[(4r,5r)-2-[(2r)-1-hydroxypropan-2-yl]-4-methyl-8-(4-methylpent-1-ynyl)-1,1-dioxo-4,5-dihydro-3h-6,1$l^{6},2-benzoxathiazocin-5-yl]methyl]-n-methyl-2-pyrazinecarboxamide
plx-4720
chebi:119735
pdk1 inhibitor
pf-573228
jq1 + mk-0752
cay10594
fti-277
gw843682x
sz4ta2
bix 02189
chembl2180739
chm-1
s-trityl-l-cysteine
qs11
hg6-64-1
lomeguatrib
rad51 inhibitor b02
bms-509744
bms614
brd-k09587429-001-01-3
gsk429286a
bx-912
fawugygebhaqbu-ppexnqrjsa-n
schembl2066172
schembl618594
ethyl 5,5,7,7-tetramethyl-2-(5-nitrothiophene-2-carboxamido)-4,5,6,7-tetrahydrothieno[2,3-c]pyridine-3-carboxylate
ar-42
fttyfnwrwdlflp-uhfffaoysa-n
n'-(2-pyrrolylidenemethyl)-2-(2,4,6-trichlorophenoxy)acetohydrazide
methylstat
nsc87877
opahmanwvumwaw-ghfzsmqjsa-n
wp-1130
dacarbazine
schembl6874948
mk-0752
brd-k35716340-001-01-2
brd-a63646118-001-02-6
brd-k62801835-001-01-0
shikonin
nsc 23766
selisistat
brd-k50799972-001-01-3
homoharringtonine
unc0321
sr8278
erastin
schembl12180851
schembl1710881
brd-k17060750-001-01-0
dbeq
jq1 + schembl2671349
mnulegdcpyonbu-pamdcedjsa-n
schembl13833463
gdc-0879
mdivi-1
chembl515416
brd-k53792571-003-01-6
n-[(2r,3r)-5-[(2s)-1-hydroxypropan-2-yl]-3-methyl-2-[[methyl-[(1-naphthalenylamino)-oxomethyl]amino]methyl]-6-oxo-3,4-dihydro-2h-1,5-benzoxazocin-10-yl]-4-pyridinecarboxamide
n-[[(2s,3s)-8-[2-(1-hydroxycyclopentyl)ethynyl]-5-[(2s)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-3,4-dihydro-2h-pyrido[2,3-b][1,5]oxazocin-2-yl]methyl]-n-methyl-4-oxanecarboxamide
gsk4112
mkwlqyduwjbeku-lwsjdiafsa-n
chembl3183639
khs101
ch 55
narciclasine
betulinic acid
sb-590885
brd-k20514654-001-01-8
dacinostat
unc0638
nsc207895
tw-37
11-cis retinoic acid
bibr 1532
dichloroplatinum diammoniate
5-azacytidine
gqrreykspjmlaw-ygnumjmvsa-n
chembl2058177
c6 ceramide
camptothecin
schembl12474870
agk2
ski ii
chembl24850
icg-001
n-[(2s,3s)-2-[(dimethylamino)methyl]-5-[(2r)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-3,4-dihydro-2h-1,5-benzoxazocin-10-yl]-2,5-dimethyl-3-pyrazolecarboxamide
vx-11e
cct 018159
wfygwjxipugujf-uhfffaoysa-n
mls000571394
aacocf3
naphtho(2,1-b)furan, 1-methyl-2-nitro-
n-[(2s,3s)-5-[(2r)-1-hydroxypropan-2-yl]-3-methyl-2-[[methyl-[(1-naphthalenylamino)-oxomethyl]amino]methyl]-6-oxo-2,3,4,7-tetrahydro-1,5-benzoxazonin-9-yl]-4-pyridinecarboxamide
brd-k25737009-001-01-2
jnk inhibitor viii
spautin-1
ic-87114
sr1001
16beta-bromoandrosterone
schembl2586580
nvp-tae684
cgp-60474
jw 480
fhtvasvneuemiv-lwsjdiafsa-n
ethyl 4-[4-[(5-nitrofuran-2-yl)methylidene]-3,5-dioxopyrazolidin-1-yl]benzoate
dmog
schembl15422095
chembl2132053
brd-k02251932-001-01-3
bms-536924;cc1=cc(=cc2=c1n/c(=c\3/c(=cc=nc3=o)nc[c@h](c4=cc(=cc=c4)cl)o)/n2)n5ccocc5
nsc60043
ng25
cid-2858522
schembl13737661
ikk-3 inhibitor
brd7880
smer-3
cx-5461
am580
elesclomol
chembl2381520
hc-067047
schembl17821363
niclosamide
n-[(2r,3s)-5-[(2r)-1-hydroxypropan-2-yl]-3-methyl-2-(methylaminomethyl)-6-oxo-2,3,4,7-tetrahydro-1,5-benzoxazonin-9-yl]methanesulfonamide
thapsigargin
brd1172
schembl1914213
i-bet-762
brd-k58306044-001-01-3
a-770041
chembl585951
glutaminase c-in-1
ql47
chembl2143553
cytochalasin b
n1-[2-(1h-indol-3-yl)ethyl]-n3-pyridin-4-ylbenzene-1,3-diamine
curcumin, curcuma longa l.
pifithrin
schembl16479156
chlorambucil
schembl15444220
zinc113660258
yk-4-279
pci-34051
fh535
schembl916391
n-(2-(4-(2-oxo-2,3-dihydro-1h-benzo[d]imidazol-1-yl)piperidin-1-yl)ethyl)-2-naphthamide
brd-k30019337-001-01-1
nvp-bsk805
chembl2356172
unc-0638 + schembl2671349
ki8751
az3146
isx-9
schembl13741284
ku-0063794
sb-431542
tpca-1
pdipalloxofubu-uhfffaoysa-n
(-)-epigallocatechin gallate
bleomycin
rsk inhibitor fmk
n9-isopropyl-olomoucine
mgcd-265
ml031
mg-132
n-[3-(1h-benzimidazol-2-yl)-5-(1-piperazinylmethyl)phenyl]-2-quinoxalinecarboxamide
le 135
phloretin
schembl2085358
abt-737
jw74
pf 750
chembl436817
cp-466722
myricetin
kcbbhekxehmwfw-yqzfvpmhsa-n
gw-405833
brd-6929
brd-k52037352-001-01-6
stemregenin 1
iu-1
apicidin
spox1_002925
brd6708
darinaparsin
wpttvjltnawyao-cdypjpissa-n
mi-2
ak174031
chebi:94975
nsc373989
snx-2112
ac-55649
gsk-650394
cyclopamine
o6-benzylguanine
pd153035
ipa-3
chir-99021
n-[[(4s,5r)-8-[2-(2-fluorophenyl)ethynyl]-2-[(2s)-1-hydroxypropan-2-yl]-4-methyl-1,1-dioxo-4,5-dihydro-3h-6,1$l^{6},2-benzoxathiazocin-5-yl]methyl]-n-methyl-2-pyridin-4-ylacetamide
su11274
chebi:94110
brd-k49456190-001-01-0
cct007093
schembl18216694
n-[(2r,3s)-5-[(2s)-1-hydroxypropan-2-yl]-3-methyl-2-(methylaminomethyl)-6-oxo-3,4-dihydro-2h-1,5-benzoxazocin-8-yl]cyclohexanecarboxamide
schembl18188080
cdk9 inhibitor
(s)-selisistat
parthenolide;c/c/1=c\cc[c@@]2([c@h](o2)[c@@h]3[c@@h](cc1)c(=c)c(=o)o3)c
marinopyrrole a
brd-a34462049-001-01-0
etp-46464
lsm-6185
bx-795
nu-7441
n-[(2s,3s,6r)-2-(hydroxymethyl)-6-[2-(4-methyl-1-piperazinyl)-2-oxoethyl]-3-oxanyl]-1,3-benzodioxole-5-carboxamide
xhqlywyicdktpj-uhfffaoysa-n
cay10603
nelarabine
parbendazole
brd-k33199242-001-01-2
mln2480
lsm-13729
tubastatin a
kpt-185
bai1
ci 976
importazole
brd-k04800985-001-01-1
gw 441756
schembl16273428
mitomycin c
i-bet151
as-605240
pd318088
cil56
brd-k02492147-001-01-4
azd1152-hqpa
brd-k27986637-001-01-3
ethyl 2-cyano-3-(3,4-dichlorophenyl)acryloylcarbamate
tcmdc-125552
bms-345541;cc1=cc2=c(c=c1)n=c(c3=nc=c(n23)c)nccn
bay 61-3606 + hydrochloric acid
bryostatin 1
bam7
palmostatin b
serdemetan
jq1 + unc0638
n-[2-methyl-5-[oxo-[3-(1-oxoprop-2-enylamino)-5-(trifluoromethyl)anilino]methyl]phenyl]-5-isoxazolecarboxamide
oprea1_718426
tipifarnib (s enantiomer)
salermide
sch-529074
pf-543
sb-525334
ro-3306
pf-4708671
gsk461364
pf 184
lrlwxbhfpgsuox-hhkxydnmsa-n
bendamustine
chembl416418
pik-93
chembl568305
chembl3188232
ku-55933
6-bio
ku-60019
schembl16296919
ak174031 + mk-1775
ml311
schembl18426910
vaf347
bms-536924;cc1=cc(=cc2=c1nc(=c3c(=cc=nc3=o)nc[c@h](c4=cc(=cc=c4)cl)o)n2)n5ccocc5
sl-0101
mps1-in-1 + hydrochloric acid
nsc 95397
lfm-a13
schembl16046542
telomerase inhibitor ix
mls000106215
procarbazine
gnf-2
fqi1
brd-a28105619-001-01-3
ly2183240
embelin
mtlmdzjugdutcp-ywefrbeisa-n
cot inhibitor-2
isoliquiritigenin
n-[3-[[2-[[4-(dimethylamino)cyclohexyl]amino]-9-propan-2-yl-6-purinyl]amino]phenyl]-2-propenamide
bms270394
nsc74859
austocystin d
schembl15428380
ku-0060648
dasa-58
n-[(2r,3s)-5-[(2r)-1-hydroxypropan-2-yl]-3-methyl-2-(methylaminomethyl)-6-oxo-2,3,4,7-tetrahydro-1,5-benzoxazonin-9-yl]-3-(4-morpholinyl)propanamide
brd-a59431241-001-01-1
whi-p97
brd-a15100685-001-01-8
schembl4463213
osi-027;coc1=cc=cc2=c/c(=c/3\c4=c(n=cnn4c(=n3)c5ccc(cc5)c(=o)o)n)/n=c21
aica ribonucleotide
n-[(2s,3s)-2-[[[(cyclohexylamino)-oxomethyl]-methylamino]methyl]-5-[(2r)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-3,4-dihydro-2h-1,5-benzoxazocin-8-yl]-2-(1-methyl-3-indolyl)acetamide
chs-828
ifosfamide
sepantronium + bromide
bleomycin sulfate
gsk1904529a
ouabain
n-[(2r,3s)-2-[[(4-chlorophenyl)sulfonyl-methylamino]methyl]-5-[(2s)-1-hydroxypropan-2-yl]-3-methyl-6-oxo-2,3,4,7-tetrahydro-1,5-benzoxazonin-9-yl]-4,4,4-trifluorobutanamide
1009820-21-6
azanide; dichloroplatinum(2+)
brd3308
leptomycin b
as601245
erk5-in-1
phenformin
rg108
at7867
chebi:93385
tcmdc-123515
stf-31
chembl2062550
n-[2-methyl-5-[2-oxo-9-(1h-pyrazol-4-yl)-1-benzo[h][1,6]naphthyridinyl]phenyl]-2-propenamide
brd-k16147474-001-01-1
ubrvgbldxdoetm-uhfffaoysa-n
qfjcirlumzquot-laoshscvsa-n
ml-210
necrosulfonamide
a-804598
schembl15422028
schembl14934014
cdk4/6 inhibitor iv
srlvtmsbrcmody-qxpfvdmisa-n
j3.559.058g
tgx-221
gw2580
schembl6465274
nsc136476
c646
mls001198989
hms1361j12
ciclopirox
schembl10183194
salubrinal
ko 143
z-llnle-cho
lsm-6189
temozolomide
chembl258148
n-(4-methoxyphenyl)sulfonyl-n-[2-[2-(1-oxido-4-pyridin-1-iumyl)ethenyl]phenyl]acetamide
necrostatin-1
n'-[(6-oxo-5-prop-2-enyl-1-cyclohexa-2,4-dienylidene)methyl]-2-[4-(phenylmethyl)-1-piperazinyl]acetohydrazide;c=ccc1=cc=cc(=cnnc(=o)cn2ccn(cc2)cc3=cc=cc=c3)c1=o
nsc48300
lrrk2-in-1
t0901317
n-cyclopropyl-3-[3-[[cyclopropyl(oxo)methyl]amino]-1h-indazol-6-yl]benzamide
jw-55
pf-4800567
az-628
fr-180204
wz4002
schembl12182311
brd4770
brd-k41597374-001-01-7
eht-1864
n'-[(6-oxo-5-prop-2-enyl-1-cyclohexa-2,4-dienylidene)methyl]-2-[4-(phenylmethyl)-1-piperazinyl]acetohydrazide;c=ccc1=cc=c/c(=c/nnc(=o)cn2ccn(cc2)cc3=cc=cc=c3)/c1=o
wz8040
ahpn
chembl2152368
epz004777 + schembl2671349
nan + nan
schembl4320913
lsm-36779
brd-k29086754-001-01-7
a-443654
sepantronium
isoevodiamine
retinol + schembl2671349
sch-529074 + jnj-26854165
agwauacrbaqpjj-uhfffaoysa-n
chembl520231
schembl11942935
chembl3185999
n-methyl-n-[4-[[6-[[1-(1-oxoprop-2-enyl)-3-piperidinyl]amino]-7h-purin-2-yl]amino]phenyl]propanamide
parthenolide;c/c/1=c/cc[c@@]2([c@h](o2)[c@@h]3[c@@h](cc1)c(=c)c(=o)o3)c
ym-201636
schembl2671349
smr000198998
rigosertib
dqnfqthsdkxsee-qfzqxzrasa-n
cbb1007
oqhlpaawwgdxaw-uhfffaoysa-n
daporinad
cyclophosphamide
mi-1
schembl12041987
chembl2206358
brd-1240
isonicotinohydroxamic acid
rsl3
schembl13833318
1,2-cyclohexanediamine anion + oxalic acid
sb-216763
brd-k05870596-001-01-4
dnmdp-2
n-[6-(2-amino-4-fluoroanilino)-6-oxohexyl]-4-methylbenzamide
chembl2398212
akt inhibitor viii
Study drug with unknown MOA
Choose drug with unknown MOA…
combobox_u = interactive(f, drug=widgets.Combobox(options=unknown))predictions_nodes = []
for goterm in list(platt_matrix.index):
predictions_nodes.append(goterm+"_"+str(1))# add names to go terms
real_go_info_svm= real_go_info[real_go_info.GO_term.isin(predictions_nodes)]
real_go_info_svm.GO_term = real_go_info_svm.GO_term.str.replace("_1","")display(combobox_u)selected_drug_u_name = combobox_u.resultpredictions_df = pd.DataFrame.from_dict(preds_unknown.loc[:,selected_drug_u_name]).reset_index()
predictions_df.columns = ["GO_term","predictions"]probabilities_df = pd.DataFrame.from_dict(probabilities_unknown.loc[:,selected_drug_u_name]).reset_index()
probabilities_df.columns = ["GO_term","probability"]
probabilities_df = probabilities_df.merge(real_go_info_svm, on="GO_term")
probabilities_df = probabilities_df.merge(predictions_df, on="GO_term")
probabilities_df.loc[probabilities_df["layer_number"] <=3].sort_values(by=["probability"], ascending=False).head(200)| GO_term | probability | Name | layer_number | predictions | |
|---|---|---|---|---|---|
| 820 | GO:0033993 | 0.809556 | Response to lipid (1) | 3.0 | 1.0 |
| 223 | GO:0018108 | 0.783375 | Peptidyl-tyrosine phosphorylation (1) | 3.0 | 1.0 |
| 573 | GO:0010629 | 0.742615 | Negative regulation of gene expression (1) | 3.0 | 1.0 |
| 106 | GO:0071900 | 0.725062 | Regulation of protein serine/threonine kinase activity (1) | 2.0 | 1.0 |
| 624 | GO:0010628 | 0.702924 | Positive regulation of gene expression (1) | 3.0 | 1.0 |
| 74 | GO:0001817 | 0.687600 | Regulation of cytokine production (1) | 3.0 | 1.0 |
| 44 | GO:0048812 | 0.672129 | Neuron projection morphogenesis (1) | 3.0 | 1.0 |
| 224 | GO:0046777 | 0.661031 | Protein autophosphorylation (1) | 1.0 | 1.0 |
| 99 | GO:0001934 | 0.658374 | Positive regulation of protein phosphorylation (1) | 3.0 | 1.0 |
| 570 | GO:0045597 | 0.628072 | Positive regulation of cell differentiation (1) | 3.0 | 1.0 |
| 839 | GO:0031047 | 0.553888 | Gene silencing by rna (1) | 2.0 | 1.0 |
| 100 | GO:0033138 | 0.542770 | Positive regulation of peptidyl-serine phosphorylation (1) | 1.0 | 1.0 |
| 888 | GO:0034976 | 0.540483 | Response to endoplasmic reticulum stress (1) | 3.0 | 1.0 |
| 633 | GO:0051301 | 0.535293 | Cell division (1) | 2.0 | 1.0 |
| 821 | GO:0034097 | 0.533600 | Response to cytokine (1) | 3.0 | 1.0 |
| 423 | GO:1902533 | 0.530199 | Positive regulation of intracellular signal transduction (1) | 2.0 | 1.0 |
| 596 | GO:0060341 | 0.523390 | Regulation of cellular localization (1) | 3.0 | 0.0 |
| 729 | GO:0120035 | 0.514954 | Regulation of plasma membrane bounded cell projection organization (1) | 3.0 | 1.0 |
| 568 | GO:0008284 | 0.513916 | Positive regulation of cell population proliferation (1) | 2.0 | 1.0 |
| 558 | GO:0016032 | 0.500000 | Viral process (1) | 3.0 | 1.0 |
| 641 | GO:0071417 | 0.494543 | Cellular response to organonitrogen compound (1) | 3.0 | 1.0 |
| 353 | GO:0006954 | 0.482633 | Inflammatory response (1) | 3.0 | 1.0 |
| 9 | GO:0043408 | 0.476159 | Regulation of mapk cascade (1) | 2.0 | 1.0 |
| 808 | GO:1902532 | 0.449154 | Negative regulation of intracellular signal transduction (1) | 3.0 | 1.0 |
| 11 | GO:0043406 | 0.445845 | Positive regulation of map kinase activity (1) | 1.0 | 1.0 |
| 8 | GO:0000165 | 0.443783 | Mapk cascade (1) | 3.0 | 0.0 |
| 358 | GO:0007005 | 0.427023 | Mitochondrion organization (1) | 3.0 | 1.0 |
| 134 | GO:0002366 | 0.423235 | Leukocyte activation involved in immune response (1) | 3.0 | 1.0 |
| 221 | GO:0018105 | 0.422261 | Peptidyl-serine phosphorylation (1) | 2.0 | 0.0 |
| 285 | GO:0051051 | 0.421500 | Negative regulation of transport (1) | 3.0 | 1.0 |
| 191 | GO:0045944 | 0.420524 | Positive regulation of transcription by rna polymerase ii (1) | 2.0 | 1.0 |
| 654 | GO:0090398 | 0.416328 | Cellular senescence (1) | 1.0 | 1.0 |
| 847 | GO:0045055 | 0.414665 | Regulated exocytosis (1) | 2.0 | 1.0 |
| 342 | GO:2001243 | 0.411574 | Negative regulation of intrinsic apoptotic signaling pathway (1) | 2.0 | 1.0 |
| 510 | GO:0042063 | 0.408279 | Gliogenesis (1) | 3.0 | 1.0 |
| 788 | GO:0009410 | 0.407937 | Response to xenobiotic stimulus (1) | 2.0 | 1.0 |
| 824 | GO:0071363 | 0.407544 | Cellular response to growth factor stimulus (1) | 3.0 | 1.0 |
| 496 | GO:0048608 | 0.406699 | Reproductive structure development (1) | 2.0 | 1.0 |
| 528 | GO:0007565 | 0.406022 | Female pregnancy (1) | 2.0 | 1.0 |
| 896 | GO:0097193 | 0.400210 | Intrinsic apoptotic signaling pathway (1) | 3.0 | 1.0 |
| 76 | GO:0001819 | 0.388417 | Positive regulation of cytokine production (1) | 2.0 | 1.0 |
| 48 | GO:0001525 | 0.386728 | Angiogenesis (1) | 2.0 | 1.0 |
| 300 | GO:0032386 | 0.384834 | Regulation of intracellular transport (1) | 2.0 | 1.0 |
| 906 | GO:0043549 | 0.378711 | Regulation of kinase activity (1) | 3.0 | 0.0 |
| 662 | GO:0031648 | 0.377411 | Protein destabilization (1) | 0.0 | 1.0 |
| 516 | GO:0007423 | 0.372363 | Sensory organ development (1) | 3.0 | 1.0 |
| 461 | GO:0050804 | 0.367884 | Modulation of chemical synaptic transmission (1) | 3.0 | 1.0 |
| 104 | GO:0006469 | 0.364467 | Negative regulation of protein kinase activity (1) | 2.0 | 1.0 |
| 620 | GO:0051098 | 0.363823 | Regulation of binding (1) | 3.0 | 1.0 |
| 86 | GO:0072006 | 0.358531 | Nephron development (1) | 2.0 | 1.0 |
| 473 | GO:0008584 | 0.350294 | Male gonad development (1) | 1.0 | 1.0 |
| 536 | GO:0007610 | 0.346612 | Behavior (1) | 3.0 | 0.0 |
| 693 | GO:1904646 | 0.344997 | Cellular response to amyloid-beta (1) | 0.0 | 1.0 |
| 376 | GO:0007015 | 0.344878 | Actin filament organization (1) | 3.0 | 1.0 |
| 552 | GO:0033365 | 0.343027 | Protein localization to organelle (1) | 3.0 | 1.0 |
| 774 | GO:0030216 | 0.342203 | Keratinocyte differentiation (1) | 2.0 | 1.0 |
| 639 | GO:0060326 | 0.341919 | Cell chemotaxis (1) | 2.0 | 1.0 |
| 154 | GO:0050778 | 0.335649 | Positive regulation of immune response (1) | 3.0 | 0.0 |
| 480 | GO:0048565 | 0.333289 | Digestive tract development (1) | 1.0 | 1.0 |
| 794 | GO:0043434 | 0.332497 | Response to peptide hormone (1) | 3.0 | 1.0 |
| 651 | GO:0050808 | 0.322913 | Synapse organization (1) | 3.0 | 1.0 |
| 772 | GO:0060485 | 0.320964 | Mesenchyme development (1) | 3.0 | 1.0 |
| 406 | GO:0048041 | 0.319689 | Focal adhesion assembly (1) | 1.0 | 1.0 |
| 698 | GO:0070663 | 0.318299 | Regulation of leukocyte proliferation (1) | 2.0 | 1.0 |
| 804 | GO:0030855 | 0.317904 | Epithelial cell differentiation (1) | 3.0 | 0.0 |
| 538 | GO:0048266 | 0.308838 | Behavioral response to pain (1) | 0.0 | 1.0 |
| 225 | GO:0006470 | 0.305848 | Protein dephosphorylation (1) | 3.0 | 1.0 |
| 719 | GO:0043244 | 0.305739 | Regulation of protein-containing complex disassembly (1) | 2.0 | 1.0 |
| 725 | GO:0070997 | 0.305130 | Neuron death (1) | 2.0 | 0.0 |
| 532 | GO:0007596 | 0.304871 | Blood coagulation (1) | 3.0 | 1.0 |
| 806 | GO:0051056 | 0.302828 | Regulation of small gtpase mediated signal transduction (1) | 3.0 | 1.0 |
| 433 | GO:0097191 | 0.302625 | Extrinsic apoptotic signaling pathway (1) | 3.0 | 1.0 |
| 574 | GO:0008285 | 0.300631 | Negative regulation of cell population proliferation (1) | 3.0 | 0.0 |
| 49 | GO:0001569 | 0.300115 | Branching involved in blood vessel morphogenesis (1) | 0.0 | 1.0 |
| 926 | GO:0060020 | 0.297104 | Bergmann glial cell differentiation (1) | 0.0 | 1.0 |
| 789 | GO:0009416 | 0.295539 | Response to light stimulus (1) | 2.0 | 0.0 |
| 887 | GO:0034504 | 0.294393 | Protein localization to nucleus (1) | 2.0 | 1.0 |
| 169 | GO:0002764 | 0.292841 | Immune response-regulating signaling pathway (1) | 3.0 | 0.0 |
| 890 | GO:0071353 | 0.290912 | Cellular response to interleukin-4 (1) | 1.0 | 1.0 |
| 505 | GO:0007283 | 0.290337 | Spermatogenesis (1) | 2.0 | 1.0 |
| 513 | GO:0030900 | 0.289030 | Forebrain development (1) | 3.0 | 1.0 |
| 241 | GO:0006612 | 0.288796 | Protein targeting to membrane (1) | 1.0 | 1.0 |
| 644 | GO:0071230 | 0.284292 | Cellular response to amino acid stimulus (1) | 1.0 | 1.0 |
| 712 | GO:0033002 | 0.280632 | Muscle cell proliferation (1) | 2.0 | 1.0 |
| 317 | GO:0006897 | 0.277768 | Endocytosis (1) | 3.0 | 1.0 |
| 230 | GO:0030162 | 0.277751 | Regulation of proteolysis (1) | 3.0 | 0.0 |
| 607 | GO:0042391 | 0.276380 | Regulation of membrane potential (1) | 3.0 | 0.0 |
| 689 | GO:1905897 | 0.275961 | Regulation of response to endoplasmic reticulum stress (1) | 2.0 | 1.0 |
| 105 | GO:0045860 | 0.275729 | Positive regulation of protein kinase activity (1) | 2.0 | 0.0 |
| 198 | GO:0006260 | 0.275261 | Dna replication (1) | 3.0 | 1.0 |
| 548 | GO:1903829 | 0.274893 | Positive regulation of protein localization (1) | 3.0 | 0.0 |
| 934 | GO:0051258 | 0.274092 | Protein polymerization (1) | 3.0 | 1.0 |
| 378 | GO:0031532 | 0.271109 | Actin cytoskeleton reorganization (1) | 1.0 | 1.0 |
| 216 | GO:0045727 | 0.270882 | Positive regulation of translation (1) | 1.0 | 1.0 |
| 642 | GO:0034599 | 0.270088 | Cellular response to oxidative stress (1) | 3.0 | 0.0 |
| 767 | GO:0051146 | 0.266722 | Striated muscle cell differentiation (1) | 2.0 | 0.0 |
| 77 | GO:0002718 | 0.262195 | Regulation of cytokine production involved in immune response (1) | 2.0 | 1.0 |
| 19 | GO:0031109 | 0.261030 | Microtubule polymerization or depolymerization (1) | 2.0 | 1.0 |
| 584 | GO:0040008 | 0.259597 | Regulation of growth (1) | 3.0 | 0.0 |
| 937 | GO:0051640 | 0.257672 | Organelle localization (1) | 3.0 | 0.0 |
| 377 | GO:0031032 | 0.257340 | Actomyosin structure organization (1) | 2.0 | 1.0 |
| 904 | GO:0042113 | 0.254618 | B cell activation (1) | 3.0 | 0.0 |
| 133 | GO:0043303 | 0.253768 | Mast cell degranulation (1) | 1.0 | 1.0 |
| 561 | GO:0048511 | 0.251652 | Rhythmic process (1) | 3.0 | 1.0 |
| 243 | GO:0006606 | 0.250144 | Protein import into nucleus (1) | 1.0 | 1.0 |
| 836 | GO:1901987 | 0.249107 | Regulation of cell cycle phase transition (1) | 3.0 | 0.0 |
| 758 | GO:0031099 | 0.247922 | Regeneration (1) | 2.0 | 1.0 |
| 739 | GO:1902903 | 0.245529 | Regulation of supramolecular fiber organization (1) | 3.0 | 1.0 |
| 323 | GO:0016236 | 0.245469 | Macroautophagy (1) | 3.0 | 1.0 |
| 478 | GO:0048568 | 0.244894 | Embryonic organ development (1) | 3.0 | 0.0 |
| 103 | GO:0042531 | 0.243583 | Positive regulation of tyrosine phosphorylation of stat protein (1) | 0.0 | 1.0 |
| 598 | GO:0043254 | 0.240098 | Regulation of protein-containing complex assembly (1) | 3.0 | 1.0 |
| 864 | GO:0030183 | 0.236405 | B cell differentiation (1) | 1.0 | 0.0 |
| 301 | GO:0032388 | 0.235842 | Positive regulation of intracellular transport (1) | 1.0 | 0.0 |
| 692 | GO:0010595 | 0.235403 | Positive regulation of endothelial cell migration (1) | 2.0 | 1.0 |
| 865 | GO:0030217 | 0.234830 | T cell differentiation (1) | 3.0 | 0.0 |
| 20 | GO:0070507 | 0.234661 | Regulation of microtubule cytoskeleton organization (1) | 2.0 | 1.0 |
| 111 | GO:0031069 | 0.234249 | Hair follicle morphogenesis (1) | 0.0 | 1.0 |
| 363 | GO:0051494 | 0.232679 | Negative regulation of cytoskeleton organization (1) | 2.0 | 1.0 |
| 226 | GO:0035304 | 0.232643 | Regulation of protein dephosphorylation (1) | 2.0 | 1.0 |
| 913 | GO:0090630 | 0.231968 | Activation of gtpase activity (1) | 0.0 | 1.0 |
| 733 | GO:0030335 | 0.231192 | Positive regulation of cell migration (1) | 3.0 | 0.0 |
| 196 | GO:0006357 | 0.230696 | Regulation of transcription by rna polymerase ii (1) | 3.0 | 1.0 |
| 524 | GO:0007519 | 0.230417 | Skeletal muscle tissue development (1) | 2.0 | 1.0 |
| 53 | GO:0001570 | 0.230416 | Vasculogenesis (1) | 1.0 | 1.0 |
| 26 | GO:1901990 | 0.229708 | Regulation of mitotic cell cycle phase transition (1) | 2.0 | 0.0 |
| 33 | GO:0000423 | 0.229518 | Mitophagy (1) | 1.0 | 1.0 |
| 615 | GO:0035265 | 0.227576 | Organ growth (1) | 2.0 | 0.0 |
| 84 | GO:0001822 | 0.227091 | Kidney development (1) | 3.0 | 0.0 |
| 151 | GO:0006959 | 0.225795 | Humoral immune response (1) | 2.0 | 1.0 |
| 244 | GO:0042307 | 0.225164 | Positive regulation of protein import into nucleus (1) | 0.0 | 1.0 |
| 24 | GO:0007346 | 0.224934 | Regulation of mitotic cell cycle (1) | 3.0 | 0.0 |
| 162 | GO:0060374 | 0.223666 | Mast cell differentiation (1) | 0.0 | 1.0 |
| 533 | GO:0030168 | 0.219923 | Platelet activation (1) | 2.0 | 1.0 |
| 152 | GO:0045087 | 0.218376 | Innate immune response (1) | 3.0 | 0.0 |
| 523 | GO:0007517 | 0.217930 | Muscle organ development (1) | 3.0 | 0.0 |
| 138 | GO:0002683 | 0.217495 | Negative regulation of immune system process (1) | 3.0 | 0.0 |
| 319 | GO:0006909 | 0.217477 | Phagocytosis (1) | 2.0 | 1.0 |
| 18 | GO:0000226 | 0.216530 | Microtubule cytoskeleton organization (1) | 3.0 | 0.0 |
| 622 | GO:0043086 | 0.212387 | Negative regulation of catalytic activity (1) | 3.0 | 1.0 |
| 898 | GO:0035924 | 0.211788 | Cellular response to vascular endothelial growth factor stimulus (1) | 2.0 | 1.0 |
| 187 | GO:0071897 | 0.211315 | Dna biosynthetic process (1) | 2.0 | 0.0 |
| 517 | GO:0043586 | 0.209986 | Tongue development (1) | 1.0 | 1.0 |
| 606 | GO:0048638 | 0.209981 | Regulation of developmental growth (1) | 2.0 | 0.0 |
| 736 | GO:0009617 | 0.209127 | Response to bacterium (1) | 3.0 | 0.0 |
| 691 | GO:0043542 | 0.207450 | Endothelial cell migration (1) | 3.0 | 0.0 |
| 321 | GO:0010507 | 0.205494 | Negative regulation of autophagy (1) | 1.0 | 1.0 |
| 149 | GO:0050853 | 0.205417 | B cell receptor signaling pathway (1) | 1.0 | 0.0 |
| 885 | GO:1900180 | 0.205300 | Regulation of protein localization to nucleus (1) | 1.0 | 1.0 |
| 907 | GO:0051881 | 0.202707 | Regulation of mitochondrial membrane potential (1) | 1.0 | 0.0 |
| 171 | GO:0003014 | 0.201637 | Renal system process (1) | 2.0 | 1.0 |
| 211 | GO:0031507 | 0.200984 | Heterochromatin assembly (1) | 1.0 | 1.0 |
| 64 | GO:0071456 | 0.200564 | Cellular response to hypoxia (1) | 1.0 | 0.0 |
| 694 | GO:0032869 | 0.197755 | Cellular response to insulin stimulus (1) | 2.0 | 0.0 |
| 779 | GO:0008544 | 0.196206 | Epidermis development (1) | 3.0 | 0.0 |
| 634 | GO:0061024 | 0.193065 | Membrane organization (1) | 2.0 | 0.0 |
| 50 | GO:0002040 | 0.192737 | Sprouting angiogenesis (1) | 1.0 | 0.0 |
| 610 | GO:0031333 | 0.191284 | Negative regulation of protein-containing complex assembly (1) | 2.0 | 1.0 |
| 657 | GO:0045165 | 0.191259 | Cell fate commitment (1) | 3.0 | 0.0 |
| 435 | GO:0016055 | 0.190638 | Wnt signaling pathway (1) | 2.0 | 0.0 |
| 569 | GO:0030307 | 0.190101 | Positive regulation of cell growth (1) | 2.0 | 1.0 |
| 200 | GO:0006281 | 0.189187 | Dna repair (1) | 2.0 | 0.0 |
| 63 | GO:0001666 | 0.188575 | Response to hypoxia (1) | 2.0 | 1.0 |
| 560 | GO:0043473 | 0.188137 | Pigmentation (1) | 2.0 | 1.0 |
| 521 | GO:0035051 | 0.187551 | Cardiocyte differentiation (1) | 2.0 | 1.0 |
| 690 | GO:2001020 | 0.187450 | Regulation of response to dna damage stimulus (1) | 2.0 | 0.0 |
| 346 | GO:0006936 | 0.187431 | Muscle contraction (1) | 3.0 | 1.0 |
| 328 | GO:0043065 | 0.184805 | Positive regulation of apoptotic process (1) | 2.0 | 0.0 |
| 650 | GO:0034329 | 0.183554 | Cell junction assembly (1) | 2.0 | 0.0 |
| 262 | GO:0045429 | 0.183059 | Positive regulation of nitric oxide biosynthetic process (1) | 0.0 | 1.0 |
| 117 | GO:0060562 | 0.183024 | Epithelial tube morphogenesis (1) | 2.0 | 0.0 |
| 583 | GO:0032967 | 0.182852 | Positive regulation of collagen biosynthetic process (1) | 0.0 | 1.0 |
| 208 | GO:0006325 | 0.182003 | Chromatin organization (1) | 3.0 | 0.0 |
| 209 | GO:0006338 | 0.181855 | Chromatin remodeling (1) | 2.0 | 1.0 |
| 882 | GO:0032147 | 0.181843 | Activation of protein kinase activity (1) | 1.0 | 0.0 |
| 7 | GO:0000122 | 0.181449 | Negative regulation of transcription by rna polymerase ii (1) | 1.0 | 0.0 |
| 911 | GO:0060416 | 0.180831 | Response to growth hormone (1) | 1.0 | 1.0 |
| 121 | GO:0090050 | 0.180206 | Positive regulation of cell migration involved in sprouting angiogenesis (1) | 0.0 | 1.0 |
| 467 | GO:0009791 | 0.179613 | Post-embryonic development (1) | 1.0 | 0.0 |
| 870 | GO:0070527 | 0.179383 | Platelet aggregation (1) | 1.0 | 1.0 |
| 781 | GO:0008625 | 0.179216 | Extrinsic apoptotic signaling pathway via death domain receptors (1) | 1.0 | 1.0 |
| 702 | GO:0048146 | 0.179210 | Positive regulation of fibroblast proliferation (1) | 0.0 | 1.0 |
| 785 | GO:0009266 | 0.178944 | Response to temperature stimulus (1) | 2.0 | 1.0 |
| 316 | GO:0033157 | 0.178583 | Regulation of intracellular protein transport (1) | 1.0 | 0.0 |
| 472 | GO:0001553 | 0.178541 | Luteinization (1) | 0.0 | 1.0 |
| 174 | GO:0010613 | 0.176998 | Positive regulation of cardiac muscle hypertrophy (1) | 1.0 | 1.0 |
| 842 | GO:0071407 | 0.176595 | Cellular response to organic cyclic compound (1) | 3.0 | 0.0 |
| 834 | GO:0035195 | 0.176389 | Gene silencing by mirna (1) | 1.0 | 0.0 |
| 504 | GO:0048709 | 0.176248 | Oligodendrocyte differentiation (1) | 2.0 | 1.0 |
| 54 | GO:2001214 | 0.175971 | Positive regulation of vasculogenesis (1) | 0.0 | 1.0 |
| 600 | GO:0010632 | 0.174413 | Regulation of epithelial cell migration (1) | 3.0 | 0.0 |
| 682 | GO:0007026 | 0.174040 | Negative regulation of microtubule depolymerization (1) | 0.0 | 1.0 |
| 276 | GO:0016567 | 0.173194 | Protein ubiquitination (1) | 3.0 | 0.0 |
| 881 | GO:0031929 | 0.171840 | Tor signaling (1) | 2.0 | 1.0 |
| 52 | GO:0001541 | 0.171713 | Ovarian follicle development (1) | 1.0 | 0.0 |
| 310 | GO:0051924 | 0.171261 | Regulation of calcium ion transport (1) | 3.0 | 0.0 |
| 899 | GO:0035994 | 0.170901 | Response to muscle stretch (1) | 1.0 | 1.0 |
| 32 | GO:0000422 | 0.170612 | Autophagy of mitochondrion (1) | 2.0 | 1.0 |
| 703 | GO:0048661 | 0.169791 | Positive regulation of smooth muscle cell proliferation (1) | 1.0 | 0.0 |
| 447 | GO:0007173 | 0.169218 | Epidermal growth factor receptor signaling pathway (1) | 2.0 | 0.0 |
sum(probabilities_df["predictions"] ==1)288
sum(probabilities_df["predictions"] ==0)651
Probability < 0.5 doesn’t mean it does not belong to the class, a probability of for example 0.2 can represent a 1 (annotated to MoA)
Modify probabilities
Take into account the annotations each GO term has (general GO terms are easier to predict as they have more annotations)
For drug with unknown MOA…
sum_annotations = slim_matrix_single_neuron.T.sum()/slim_matrix_single_neuron.shape[1]
logits_apriori = np.log(sum_annotations/(1-sum_annotations))
logits_apost= np.log(probabilities_df["probability"]/(1-probabilities_df["probability"]))
delta_logits =logits_apost.to_numpy()- logits_apriori.to_numpy()
delta_logits_df = pd.DataFrame(delta_logits)
delta_logits_df.columns = ["delta_logits"]
probabilities_mod = probabilities_df.merge(delta_logits_df, left_index=True,right_index=True)probabilities_mod.loc[probabilities_mod["predictions"] ==1].loc[probabilities_mod["layer_number"] <= 7].sort_values(by=["delta_logits"], ascending=False)| GO_term | probability | Name | layer_number | predictions | delta_logits | |
|---|---|---|---|---|---|---|
| 839 | GO:0031047 | 0.553888 | Gene silencing by rna (1) | 2.0 | 1.0 | 2.192458 |
| 662 | GO:0031648 | 0.377411 | Protein destabilization (1) | 0.0 | 1.0 | 2.092834 |
| 106 | GO:0071900 | 0.725062 | Regulation of protein serine/threonine kinase activity (1) | 2.0 | 1.0 | 1.944569 |
| 33 | GO:0000423 | 0.229518 | Mitophagy (1) | 1.0 | 1.0 | 1.880007 |
| 538 | GO:0048266 | 0.308838 | Behavioral response to pain (1) | 0.0 | 1.0 | 1.857031 |
| 913 | GO:0090630 | 0.231968 | Activation of gtpase activity (1) | 0.0 | 1.0 | 1.793943 |
| 216 | GO:0045727 | 0.270882 | Positive regulation of translation (1) | 1.0 | 1.0 | 1.746070 |
| 223 | GO:0018108 | 0.783375 | Peptidyl-tyrosine phosphorylation (1) | 3.0 | 1.0 | 1.672826 |
| 719 | GO:0043244 | 0.305739 | Regulation of protein-containing complex disassembly (1) | 2.0 | 1.0 | 1.646098 |
| 241 | GO:0006612 | 0.288796 | Protein targeting to membrane (1) | 1.0 | 1.0 | 1.626841 |
| 104 | GO:0006469 | 0.364467 | Negative regulation of protein kinase activity (1) | 2.0 | 1.0 | 1.593794 |
| 888 | GO:0034976 | 0.540483 | Response to endoplasmic reticulum stress (1) | 3.0 | 1.0 | 1.575980 |
| 890 | GO:0071353 | 0.290912 | Cellular response to interleukin-4 (1) | 1.0 | 1.0 | 1.575256 |
| 638 | GO:0033554 | 0.884331 | Cellular response to stress (1) | 4.0 | 1.0 | 1.573975 |
| 820 | GO:0033993 | 0.809556 | Response to lipid (1) | 3.0 | 1.0 | 1.551573 |
| 74 | GO:0001817 | 0.687600 | Regulation of cytokine production (1) | 3.0 | 1.0 | 1.534715 |
| 682 | GO:0007026 | 0.174040 | Negative regulation of microtubule depolymerization (1) | 0.0 | 1.0 | 1.533781 |
| 528 | GO:0007565 | 0.406022 | Female pregnancy (1) | 2.0 | 1.0 | 1.478881 |
| 224 | GO:0046777 | 0.661031 | Protein autophosphorylation (1) | 1.0 | 1.0 | 1.453821 |
| 544 | GO:0060179 | 0.161090 | Male mating behavior (1) | 0.0 | 1.0 | 1.440901 |
| 32 | GO:0000422 | 0.170612 | Autophagy of mitochondrion (1) | 2.0 | 1.0 | 1.409881 |
| 774 | GO:0030216 | 0.342203 | Keratinocyte differentiation (1) | 2.0 | 1.0 | 1.406530 |
| 44 | GO:0048812 | 0.672129 | Neuron projection morphogenesis (1) | 3.0 | 1.0 | 1.385004 |
| 472 | GO:0001553 | 0.178541 | Luteinization (1) | 0.0 | 1.0 | 1.373326 |
| 77 | GO:0002718 | 0.262195 | Regulation of cytokine production involved in immune response (1) | 2.0 | 1.0 | 1.372827 |
| 900 | GO:0042060 | 0.612497 | Wound healing (1) | 4.0 | 1.0 | 1.325911 |
| 385 | GO:0060632 | 0.143694 | Regulation of microtubule-based movement (1) | 1.0 | 1.0 | 1.306104 |
| 726 | GO:0065003 | 0.654123 | Protein-containing complex assembly (1) | 4.0 | 1.0 | 1.304384 |
| 926 | GO:0060020 | 0.297104 | Bergmann glial cell differentiation (1) | 0.0 | 1.0 | 1.288696 |
| 582 | GO:1902459 | 0.140358 | Positive regulation of stem cell population maintenance (1) | 0.0 | 1.0 | 1.278722 |
| 63 | GO:0001666 | 0.188575 | Response to hypoxia (1) | 2.0 | 1.0 | 1.276928 |
| 342 | GO:2001243 | 0.411574 | Negative regulation of intrinsic apoptotic signaling pathway (1) | 2.0 | 1.0 | 1.262448 |
| 573 | GO:0010629 | 0.742615 | Negative regulation of gene expression (1) | 3.0 | 1.0 | 1.216446 |
| 480 | GO:0048565 | 0.333289 | Digestive tract development (1) | 1.0 | 1.0 | 1.203775 |
| 174 | GO:0010613 | 0.176998 | Positive regulation of cardiac muscle hypertrophy (1) | 1.0 | 1.0 | 1.199403 |
| 676 | GO:0030282 | 0.116755 | Bone mineralization (1) | 1.0 | 1.0 | 1.177409 |
| 49 | GO:0001569 | 0.300115 | Branching involved in blood vessel morphogenesis (1) | 0.0 | 1.0 | 1.170620 |
| 899 | GO:0035994 | 0.170901 | Response to muscle stretch (1) | 1.0 | 1.0 | 1.156962 |
| 443 | GO:0035860 | 0.114067 | Glial cell-derived neurotrophic factor receptor signaling pathway (1) | 0.0 | 1.0 | 1.151083 |
| 847 | GO:0045055 | 0.414665 | Regulated exocytosis (1) | 2.0 | 1.0 | 1.096844 |
| 134 | GO:0002366 | 0.423235 | Leukocyte activation involved in immune response (1) | 3.0 | 1.0 | 1.076788 |
| 181 | GO:0006139 | 0.875555 | Nucleobase-containing compound metabolic process (1) | 6.0 | 1.0 | 1.061941 |
| 689 | GO:1905897 | 0.275961 | Regulation of response to endoplasmic reticulum stress (1) | 2.0 | 1.0 | 1.052784 |
| 38 | GO:0000902 | 0.813929 | Cell morphogenesis (1) | 4.0 | 1.0 | 1.052132 |
| 654 | GO:0090398 | 0.416328 | Cellular senescence (1) | 1.0 | 1.0 | 1.048427 |
| 693 | GO:1904646 | 0.344997 | Cellular response to amyloid-beta (1) | 0.0 | 1.0 | 1.043235 |
| 100 | GO:0033138 | 0.542770 | Positive regulation of peptidyl-serine phosphorylation (1) | 1.0 | 1.0 | 1.018799 |
| 745 | GO:0009653 | 0.877370 | Anatomical structure morphogenesis (1) | 5.0 | 1.0 | 1.014649 |
| 198 | GO:0006260 | 0.275261 | Dna replication (1) | 3.0 | 1.0 | 1.007970 |
| 133 | GO:0043303 | 0.253768 | Mast cell degranulation (1) | 1.0 | 1.0 | 0.981406 |
| 368 | GO:0060271 | 0.126831 | Cilium assembly (1) | 3.0 | 1.0 | 0.970313 |
| 285 | GO:0051051 | 0.421500 | Negative regulation of transport (1) | 3.0 | 1.0 | 0.964316 |
| 626 | GO:0051649 | 0.802013 | Establishment of localization in cell (1) | 4.0 | 1.0 | 0.957089 |
| 378 | GO:0031532 | 0.271109 | Actin cytoskeleton reorganization (1) | 1.0 | 1.0 | 0.947004 |
| 651 | GO:0050808 | 0.322913 | Synapse organization (1) | 3.0 | 1.0 | 0.943923 |
| 11 | GO:0043406 | 0.445845 | Positive regulation of map kinase activity (1) | 1.0 | 1.0 | 0.939978 |
| 34 | GO:1903146 | 0.103490 | Regulation of autophagy of mitochondrion (1) | 1.0 | 1.0 | 0.932003 |
| 262 | GO:0045429 | 0.183059 | Positive regulation of nitric oxide biosynthetic process (1) | 0.0 | 1.0 | 0.911660 |
| 661 | GO:0046326 | 0.165116 | Positive regulation of glucose import (1) | 0.0 | 1.0 | 0.907432 |
| 911 | GO:0060416 | 0.180831 | Response to growth hormone (1) | 1.0 | 1.0 | 0.896690 |
| 571 | GO:2000010 | 0.127151 | Positive regulation of protein localization to cell surface (1) | 0.0 | 1.0 | 0.888565 |
| 633 | GO:0051301 | 0.535293 | Cell division (1) | 2.0 | 1.0 | 0.887200 |
| 483 | GO:0035909 | 0.162013 | Aorta morphogenesis (1) | 1.0 | 1.0 | 0.884752 |
| 54 | GO:2001214 | 0.175971 | Positive regulation of vasculogenesis (1) | 0.0 | 1.0 | 0.863534 |
| 416 | GO:0035556 | 0.887824 | Intracellular signal transduction (1) | 4.0 | 1.0 | 0.862843 |
| 470 | GO:0042733 | 0.097137 | Embryonic digit morphogenesis (1) | 0.0 | 1.0 | 0.861598 |
| 886 | GO:0034502 | 0.132453 | Protein localization to chromosome (1) | 2.0 | 1.0 | 0.856780 |
| 111 | GO:0031069 | 0.234249 | Hair follicle morphogenesis (1) | 0.0 | 1.0 | 0.832897 |
| 880 | GO:0031667 | 0.374380 | Response to nutrient levels (1) | 4.0 | 1.0 | 0.819334 |
| 833 | GO:0010467 | 0.880677 | Gene expression (1) | 5.0 | 1.0 | 0.817357 |
| 184 | GO:0006275 | 0.102018 | Regulation of dna replication (1) | 2.0 | 1.0 | 0.816174 |
| 162 | GO:0060374 | 0.223666 | Mast cell differentiation (1) | 0.0 | 1.0 | 0.815593 |
| 265 | GO:0051247 | 0.818839 | Positive regulation of protein metabolic process (1) | 4.0 | 1.0 | 0.802282 |
| 821 | GO:0034097 | 0.533600 | Response to cytokine (1) | 3.0 | 1.0 | 0.801775 |
| 408 | GO:0007165 | 0.930074 | Signal transduction (1) | 6.0 | 1.0 | 0.801131 |
| 849 | GO:0043966 | 0.142338 | Histone h3 acetylation (1) | 2.0 | 1.0 | 0.797379 |
| 112 | GO:0060789 | 0.091132 | Hair follicle placode formation (1) | 0.0 | 1.0 | 0.791156 |
| 558 | GO:0016032 | 0.500000 | Viral process (1) | 3.0 | 1.0 | 0.785929 |
| 151 | GO:0006959 | 0.225795 | Humoral immune response (1) | 2.0 | 1.0 | 0.785159 |
| 86 | GO:0072006 | 0.358531 | Nephron development (1) | 2.0 | 1.0 | 0.777591 |
| 83 | GO:0002720 | 0.163684 | Positive regulation of cytokine production involved in immune response (1) | 1.0 | 1.0 | 0.776348 |
| 53 | GO:0001570 | 0.230416 | Vasculogenesis (1) | 1.0 | 1.0 | 0.770097 |
| 532 | GO:0007596 | 0.304871 | Blood coagulation (1) | 3.0 | 1.0 | 0.764501 |
| 567 | GO:0051641 | 0.841801 | Cellular localization (1) | 5.0 | 1.0 | 0.761481 |
| 639 | GO:0060326 | 0.341919 | Cell chemotaxis (1) | 2.0 | 1.0 | 0.758938 |
| 19 | GO:0031109 | 0.261030 | Microtubule polymerization or depolymerization (1) | 2.0 | 1.0 | 0.746075 |
| 800 | GO:0030521 | 0.102431 | Androgen receptor signaling pathway (1) | 1.0 | 1.0 | 0.729092 |
| 511 | GO:0030182 | 0.721830 | Neuron differentiation (1) | 5.0 | 1.0 | 0.726501 |
| 806 | GO:0051056 | 0.302828 | Regulation of small gtpase mediated signal transduction (1) | 3.0 | 1.0 | 0.724275 |
| 527 | GO:0007528 | 0.109807 | Neuromuscular junction development (1) | 1.0 | 1.0 | 0.722229 |
| 794 | GO:0043434 | 0.332497 | Response to peptide hormone (1) | 3.0 | 1.0 | 0.716778 |
| 99 | GO:0001934 | 0.658374 | Positive regulation of protein phosphorylation (1) | 3.0 | 1.0 | 0.708241 |
| 896 | GO:0097193 | 0.400210 | Intrinsic apoptotic signaling pathway (1) | 3.0 | 1.0 | 0.705651 |
| 624 | GO:0010628 | 0.702924 | Positive regulation of gene expression (1) | 3.0 | 1.0 | 0.704419 |
| 722 | GO:0042325 | 0.780828 | Regulation of phosphorylation (1) | 5.0 | 1.0 | 0.698909 |
| 752 | GO:0043170 | 0.942545 | Macromolecule metabolic process (1) | 7.0 | 1.0 | 0.693446 |
| 517 | GO:0043586 | 0.209986 | Tongue development (1) | 1.0 | 1.0 | 0.692360 |
| 182 | GO:0016070 | 0.778891 | Rna metabolic process (1) | 5.0 | 1.0 | 0.687621 |
| 281 | GO:0006811 | 0.500000 | Ion transport (1) | 6.0 | 1.0 | 0.686632 |
| 586 | GO:2000773 | 0.121640 | Negative regulation of cellular senescence (1) | 0.0 | 1.0 | 0.685597 |
| 808 | GO:1902532 | 0.449154 | Negative regulation of intracellular signal transduction (1) | 3.0 | 1.0 | 0.684968 |
| 570 | GO:0045597 | 0.628072 | Positive regulation of cell differentiation (1) | 3.0 | 1.0 | 0.680798 |
| 76 | GO:0001819 | 0.388417 | Positive regulation of cytokine production (1) | 2.0 | 1.0 | 0.679733 |
| 934 | GO:0051258 | 0.274092 | Protein polymerization (1) | 3.0 | 1.0 | 0.677814 |
| 713 | GO:0035726 | 0.096936 | Common myeloid progenitor cell proliferation (1) | 0.0 | 1.0 | 0.667842 |
| 386 | GO:0007049 | 0.727375 | Cell cycle (1) | 6.0 | 1.0 | 0.665704 |
| 473 | GO:0008584 | 0.350294 | Male gonad development (1) | 1.0 | 1.0 | 0.663186 |
| 813 | GO:0051898 | 0.103239 | Negative regulation of protein kinase b signaling (1) | 0.0 | 1.0 | 0.653209 |
| 698 | GO:0070663 | 0.318299 | Regulation of leukocyte proliferation (1) | 2.0 | 1.0 | 0.652091 |
| 729 | GO:0120035 | 0.514954 | Regulation of plasma membrane bounded cell projection organization (1) | 3.0 | 1.0 | 0.631426 |
| 881 | GO:0031929 | 0.171840 | Tor signaling (1) | 2.0 | 1.0 | 0.624585 |
| 496 | GO:0048608 | 0.406699 | Reproductive structure development (1) | 2.0 | 1.0 | 0.619202 |
| 559 | GO:0022414 | 0.641754 | Reproductive process (1) | 4.0 | 1.0 | 0.617772 |
| 562 | GO:0050896 | 0.963328 | Response to stimulus (1) | 7.0 | 1.0 | 0.605804 |
| 381 | GO:0008064 | 0.113103 | Regulation of actin polymerization or depolymerization (1) | 2.0 | 1.0 | 0.603158 |
| 855 | GO:1903578 | 0.141298 | Regulation of atp metabolic process (1) | 1.0 | 1.0 | 0.602865 |
| 619 | GO:0050790 | 0.825081 | Regulation of catalytic activity (1) | 4.0 | 1.0 | 0.598054 |
| 476 | GO:0048714 | 0.112165 | Positive regulation of oligodendrocyte differentiation (1) | 0.0 | 1.0 | 0.593772 |
| 363 | GO:0051494 | 0.232679 | Negative regulation of cytoskeleton organization (1) | 2.0 | 1.0 | 0.593448 |
| 652 | GO:0042180 | 0.090402 | Cellular ketone metabolic process (1) | 3.0 | 1.0 | 0.590857 |
| 423 | GO:1902533 | 0.530199 | Positive regulation of intracellular signal transduction (1) | 2.0 | 1.0 | 0.581066 |
| 319 | GO:0006909 | 0.217477 | Phagocytosis (1) | 2.0 | 1.0 | 0.578885 |
| 353 | GO:0006954 | 0.482633 | Inflammatory response (1) | 3.0 | 1.0 | 0.578336 |
| 406 | GO:0048041 | 0.319689 | Focal adhesion assembly (1) | 1.0 | 1.0 | 0.577606 |
| 585 | GO:0048589 | 0.599054 | Developmental growth (1) | 4.0 | 1.0 | 0.575879 |
| 461 | GO:0050804 | 0.367884 | Modulation of chemical synaptic transmission (1) | 3.0 | 1.0 | 0.568933 |
| 711 | GO:0019752 | 0.316625 | Carboxylic acid metabolic process (1) | 4.0 | 1.0 | 0.563481 |
| 629 | GO:0051174 | 0.783163 | Regulation of phosphorus metabolic process (1) | 6.0 | 1.0 | 0.558257 |
| 82 | GO:0032743 | 0.079738 | Positive regulation of interleukin-2 production (1) | 0.0 | 1.0 | 0.545266 |
| 510 | GO:0042063 | 0.408279 | Gliogenesis (1) | 3.0 | 1.0 | 0.539129 |
| 730 | GO:0031175 | 0.631148 | Neuron projection development (1) | 4.0 | 1.0 | 0.537147 |
| 741 | GO:0016477 | 0.682158 | Cell migration (1) | 4.0 | 1.0 | 0.536651 |
| 663 | GO:0050821 | 0.165950 | Protein stabilization (1) | 0.0 | 1.0 | 0.535214 |
| 343 | GO:1902166 | 0.145254 | Negative regulation of intrinsic apoptotic signaling pathway in response to dna damage by p53 class mediator (1) | 0.0 | 1.0 | 0.525494 |
| 213 | GO:0006396 | 0.268052 | Rna processing (1) | 4.0 | 1.0 | 0.523646 |
| 563 | GO:1900272 | 0.063979 | Negative regulation of long-term synaptic potentiation (1) | 0.0 | 1.0 | 0.517858 |
| 36 | GO:0000723 | 0.137533 | Telomere maintenance (1) | 1.0 | 1.0 | 0.515446 |
| 524 | GO:0007519 | 0.230417 | Skeletal muscle tissue development (1) | 2.0 | 1.0 | 0.511692 |
| 93 | GO:0001843 | 0.082584 | Neural tube closure (1) | 1.0 | 1.0 | 0.491845 |
| 226 | GO:0035304 | 0.232643 | Regulation of protein dephosphorylation (1) | 2.0 | 1.0 | 0.490892 |
| 684 | GO:0051770 | 0.158964 | Positive regulation of nitric-oxide synthase biosynthetic process (1) | 0.0 | 1.0 | 0.483868 |
| 583 | GO:0032967 | 0.182852 | Positive regulation of collagen biosynthetic process (1) | 0.0 | 1.0 | 0.478922 |
| 641 | GO:0071417 | 0.494543 | Cellular response to organonitrogen compound (1) | 3.0 | 1.0 | 0.475108 |
| 788 | GO:0009410 | 0.407937 | Response to xenobiotic stimulus (1) | 2.0 | 1.0 | 0.474799 |
| 412 | GO:0009966 | 0.816584 | Regulation of signal transduction (1) | 5.0 | 1.0 | 0.474348 |
| 735 | GO:0046718 | 0.151222 | Viral entry into host cell (1) | 1.0 | 1.0 | 0.472175 |
| 266 | GO:0030163 | 0.372430 | Protein catabolic process (1) | 4.0 | 1.0 | 0.453053 |
| 196 | GO:0006357 | 0.230696 | Regulation of transcription by rna polymerase ii (1) | 3.0 | 1.0 | 0.447389 |
| 840 | GO:0043154 | 0.084113 | Negative regulation of cysteine-type endopeptidase activity involved in apoptotic process (1) | 1.0 | 1.0 | 0.427212 |
| 299 | GO:0030705 | 0.133167 | Cytoskeleton-dependent intracellular transport (1) | 3.0 | 1.0 | 0.424567 |
| 433 | GO:0097191 | 0.302625 | Extrinsic apoptotic signaling pathway (1) | 3.0 | 1.0 | 0.420731 |
| 610 | GO:0031333 | 0.191284 | Negative regulation of protein-containing complex assembly (1) | 2.0 | 1.0 | 0.417629 |
| 501 | GO:0007507 | 0.480118 | Heart development (1) | 4.0 | 1.0 | 0.417365 |
| 901 | GO:0042110 | 0.432222 | T cell activation (1) | 4.0 | 1.0 | 0.413843 |
| 260 | GO:0006807 | 0.931522 | Nitrogen compound metabolic process (1) | 7.0 | 1.0 | 0.413085 |
| 785 | GO:0009266 | 0.178944 | Response to temperature stimulus (1) | 2.0 | 1.0 | 0.412491 |
| 358 | GO:0007005 | 0.427023 | Mitochondrion organization (1) | 3.0 | 1.0 | 0.412213 |
| 377 | GO:0031032 | 0.257340 | Actomyosin structure organization (1) | 2.0 | 1.0 | 0.410068 |
| 484 | GO:0007399 | 0.731066 | Nervous system development (1) | 6.0 | 1.0 | 0.409544 |
| 136 | GO:0002376 | 0.760395 | Immune system process (1) | 6.0 | 1.0 | 0.409057 |
| 211 | GO:0031507 | 0.200984 | Heterochromatin assembly (1) | 1.0 | 1.0 | 0.406540 |
| 20 | GO:0070507 | 0.234661 | Regulation of microtubule cytoskeleton organization (1) | 2.0 | 1.0 | 0.406533 |
| 356 | GO:0006996 | 0.787638 | Organelle organization (1) | 5.0 | 1.0 | 0.400534 |
| 376 | GO:0007015 | 0.344878 | Actin filament organization (1) | 3.0 | 1.0 | 0.399824 |
| 173 | GO:0003300 | 0.147997 | Cardiac muscle hypertrophy (1) | 2.0 | 1.0 | 0.399424 |
| 620 | GO:0051098 | 0.363823 | Regulation of binding (1) | 3.0 | 1.0 | 0.394294 |
| 922 | GO:0036324 | 0.117627 | Vascular endothelial growth factor receptor-2 signaling pathway (1) | 0.0 | 1.0 | 0.392320 |
| 487 | GO:0030325 | 0.087339 | Adrenal gland development (1) | 0.0 | 1.0 | 0.389651 |
| 781 | GO:0008625 | 0.179216 | Extrinsic apoptotic signaling pathway via death domain receptors (1) | 1.0 | 1.0 | 0.375455 |
| 608 | GO:0043114 | 0.067682 | Regulation of vascular permeability (1) | 1.0 | 1.0 | 0.368329 |
| 103 | GO:0042531 | 0.243583 | Positive regulation of tyrosine phosphorylation of stat protein (1) | 0.0 | 1.0 | 0.365635 |
| 102 | GO:0050731 | 0.126350 | Positive regulation of peptidyl-tyrosine phosphorylation (1) | 2.0 | 1.0 | 0.364186 |
| 504 | GO:0048709 | 0.176248 | Oligodendrocyte differentiation (1) | 2.0 | 1.0 | 0.355145 |
| 324 | GO:0016241 | 0.118988 | Regulation of macroautophagy (1) | 2.0 | 1.0 | 0.349328 |
| 218 | GO:0006468 | 0.771378 | Protein phosphorylation (1) | 5.0 | 1.0 | 0.348022 |
| 171 | GO:0003014 | 0.201637 | Renal system process (1) | 2.0 | 1.0 | 0.341557 |
| 644 | GO:0071230 | 0.284292 | Cellular response to amino acid stimulus (1) | 1.0 | 1.0 | 0.332289 |
| 321 | GO:0010507 | 0.205494 | Negative regulation of autophagy (1) | 1.0 | 1.0 | 0.332037 |
| 533 | GO:0030168 | 0.219923 | Platelet activation (1) | 2.0 | 1.0 | 0.322598 |
| 546 | GO:0008104 | 0.599923 | Protein localization (1) | 5.0 | 1.0 | 0.318133 |
| 362 | GO:0033043 | 0.644550 | Regulation of organelle organization (1) | 4.0 | 1.0 | 0.315090 |
| 851 | GO:0070933 | 0.058559 | Histone h4 deacetylation (1) | 0.0 | 1.0 | 0.313671 |
| 460 | GO:0023061 | 0.159325 | Signal release (1) | 4.0 | 1.0 | 0.312808 |
| 438 | GO:0007179 | 0.169016 | Transforming growth factor beta receptor signaling pathway (1) | 1.0 | 1.0 | 0.304505 |
| 300 | GO:0032386 | 0.384834 | Regulation of intracellular transport (1) | 2.0 | 1.0 | 0.296704 |
| 920 | GO:0036092 | 0.063107 | Phosphatidylinositol-3-phosphate biosynthetic process (1) | 0.0 | 1.0 | 0.293434 |
| 505 | GO:0007283 | 0.290337 | Spermatogenesis (1) | 2.0 | 1.0 | 0.287750 |
| 357 | GO:0006997 | 0.062583 | Nucleus organization (1) | 2.0 | 1.0 | 0.284535 |
| 748 | GO:0009056 | 0.627534 | Catabolic process (1) | 5.0 | 1.0 | 0.276960 |
| 48 | GO:0001525 | 0.386728 | Angiogenesis (1) | 2.0 | 1.0 | 0.264850 |
| 541 | GO:0008542 | 0.131766 | Visual learning (1) | 0.0 | 1.0 | 0.264386 |
| 98 | GO:0001932 | 0.669289 | Regulation of protein phosphorylation (1) | 4.0 | 1.0 | 0.263137 |
| 734 | GO:0051702 | 0.152779 | Biological process involved in interaction with symbiont (1) | 2.0 | 1.0 | 0.263093 |
| 243 | GO:0006606 | 0.250144 | Protein import into nucleus (1) | 1.0 | 1.0 | 0.261493 |
| 513 | GO:0030900 | 0.289030 | Forebrain development (1) | 3.0 | 1.0 | 0.257354 |
| 673 | GO:0043392 | 0.109472 | Negative regulation of dna binding (1) | 1.0 | 1.0 | 0.255228 |
| 346 | GO:0006936 | 0.187431 | Muscle contraction (1) | 3.0 | 1.0 | 0.250863 |
| 552 | GO:0033365 | 0.343027 | Protein localization to organelle (1) | 3.0 | 1.0 | 0.239223 |
| 9 | GO:0043408 | 0.476159 | Regulation of mapk cascade (1) | 2.0 | 1.0 | 0.238056 |
| 887 | GO:0034504 | 0.294393 | Protein localization to nucleus (1) | 2.0 | 1.0 | 0.236099 |
| 598 | GO:0043254 | 0.240098 | Regulation of protein-containing complex assembly (1) | 3.0 | 1.0 | 0.234151 |
| 280 | GO:0006810 | 0.773180 | Transport (1) | 7.0 | 1.0 | 0.229528 |
| 267 | GO:0045732 | 0.069485 | Positive regulation of protein catabolic process (1) | 2.0 | 1.0 | 0.220326 |
| 668 | GO:0010976 | 0.084952 | Positive regulation of neuron projection development (1) | 1.0 | 1.0 | 0.216502 |
| 212 | GO:0051090 | 0.131395 | Regulation of dna-binding transcription factor activity (1) | 2.0 | 1.0 | 0.215454 |
| 799 | GO:0009743 | 0.121060 | Response to carbohydrate (1) | 2.0 | 1.0 | 0.214795 |
| 675 | GO:0071277 | 0.074147 | Cellular response to calcium ion (1) | 0.0 | 1.0 | 0.211553 |
| 495 | GO:0060976 | 0.094845 | Coronary vasculature development (1) | 1.0 | 1.0 | 0.210353 |
| 803 | GO:0042475 | 0.125550 | Odontogenesis of dentin-containing tooth (1) | 2.0 | 1.0 | 0.208932 |
| 671 | GO:0032092 | 0.120001 | Positive regulation of protein binding (1) | 1.0 | 1.0 | 0.204806 |
| 772 | GO:0060485 | 0.320964 | Mesenchyme development (1) | 3.0 | 1.0 | 0.203758 |
| 121 | GO:0090050 | 0.180206 | Positive regulation of cell migration involved in sprouting angiogenesis (1) | 0.0 | 1.0 | 0.202697 |
| 801 | GO:0033143 | 0.078487 | Regulation of intracellular steroid hormone receptor signaling pathway (1) | 1.0 | 1.0 | 0.199503 |
| 870 | GO:0070527 | 0.179383 | Platelet aggregation (1) | 1.0 | 1.0 | 0.197119 |
| 314 | GO:0070588 | 0.238228 | Calcium ion transmembrane transport (1) | 4.0 | 1.0 | 0.196919 |
| 656 | GO:0030154 | 0.829241 | Cell differentiation (1) | 6.0 | 1.0 | 0.193962 |
| 645 | GO:0071300 | 0.128940 | Cellular response to retinoic acid (1) | 0.0 | 1.0 | 0.193772 |
| 897 | GO:0035767 | 0.138830 | Endothelial cell chemotaxis (1) | 1.0 | 1.0 | 0.192330 |
| 332 | GO:0097190 | 0.410188 | Apoptotic signaling pathway (1) | 4.0 | 1.0 | 0.189603 |
| 643 | GO:0071222 | 0.143456 | Cellular response to lipopolysaccharide (1) | 2.0 | 1.0 | 0.189188 |
| 898 | GO:0035924 | 0.211788 | Cellular response to vascular endothelial growth factor stimulus (1) | 2.0 | 1.0 | 0.184593 |
| 459 | GO:0007267 | 0.506882 | Cell-cell signaling (1) | 5.0 | 1.0 | 0.184371 |
| 323 | GO:0016236 | 0.245469 | Macroautophagy (1) | 3.0 | 1.0 | 0.183750 |
| 317 | GO:0006897 | 0.277768 | Endocytosis (1) | 3.0 | 1.0 | 0.178141 |
| 244 | GO:0042307 | 0.225164 | Positive regulation of protein import into nucleus (1) | 0.0 | 1.0 | 0.177873 |
| 728 | GO:0030032 | 0.140303 | Lamellipodium assembly (1) | 1.0 | 1.0 | 0.163285 |
| 640 | GO:0071310 | 0.661841 | Cellular response to organic substance (1) | 4.0 | 1.0 | 0.156044 |
| 686 | GO:0097009 | 0.055448 | Energy homeostasis (1) | 0.0 | 1.0 | 0.155920 |
| 692 | GO:0010595 | 0.235403 | Positive regulation of endothelial cell migration (1) | 2.0 | 1.0 | 0.154755 |
| 309 | GO:0034765 | 0.277608 | Regulation of ion transmembrane transport (1) | 4.0 | 1.0 | 0.153881 |
| 770 | GO:0009887 | 0.516411 | Animal organ morphogenesis (1) | 4.0 | 1.0 | 0.152678 |
| 209 | GO:0006338 | 0.181855 | Chromatin remodeling (1) | 2.0 | 1.0 | 0.147943 |
| 547 | GO:0032880 | 0.479479 | Regulation of protein localization (1) | 4.0 | 1.0 | 0.144928 |
| 918 | GO:0046854 | 0.054745 | Phosphatidylinositol phosphate biosynthetic process (1) | 1.0 | 1.0 | 0.142413 |
| 329 | GO:0043066 | 0.610305 | Negative regulation of apoptotic process (1) | 4.0 | 1.0 | 0.132954 |
| 824 | GO:0071363 | 0.407544 | Cellular response to growth factor stimulus (1) | 3.0 | 1.0 | 0.122808 |
| 603 | GO:0061045 | 0.068278 | Negative regulation of wound healing (1) | 2.0 | 1.0 | 0.122775 |
| 225 | GO:0006470 | 0.305848 | Protein dephosphorylation (1) | 3.0 | 1.0 | 0.111956 |
| 388 | GO:0051726 | 0.483775 | Regulation of cell cycle (1) | 5.0 | 1.0 | 0.109432 |
| 516 | GO:0007423 | 0.372363 | Sensory organ development (1) | 3.0 | 1.0 | 0.106514 |
| 261 | GO:0051171 | 0.836045 | Regulation of nitrogen compound metabolic process (1) | 6.0 | 1.0 | 0.100916 |
| 375 | GO:0051496 | 0.076120 | Positive regulation of stress fiber assembly (1) | 0.0 | 1.0 | 0.097110 |
| 758 | GO:0031099 | 0.247922 | Regeneration (1) | 2.0 | 1.0 | 0.096133 |
| 55 | GO:0001649 | 0.136472 | Osteoblast differentiation (1) | 1.0 | 1.0 | 0.091104 |
| 227 | GO:0032516 | 0.155024 | Positive regulation of phosphoprotein phosphatase activity (1) | 0.0 | 1.0 | 0.090968 |
| 191 | GO:0045944 | 0.420524 | Positive regulation of transcription by rna polymerase ii (1) | 2.0 | 1.0 | 0.084844 |
| 283 | GO:0051049 | 0.611523 | Regulation of transport (1) | 5.0 | 1.0 | 0.084359 |
| 739 | GO:1902903 | 0.245529 | Regulation of supramolecular fiber organization (1) | 3.0 | 1.0 | 0.083258 |
| 569 | GO:0030307 | 0.190101 | Positive regulation of cell growth (1) | 2.0 | 1.0 | 0.078819 |
| 679 | GO:0042310 | 0.060677 | Vasoconstriction (1) | 1.0 | 1.0 | 0.075349 |
| 405 | GO:0007159 | 0.162103 | Leukocyte cell-cell adhesion (1) | 3.0 | 1.0 | 0.074992 |
| 566 | GO:0032879 | 0.660318 | Regulation of localization (1) | 6.0 | 1.0 | 0.074219 |
| 568 | GO:0008284 | 0.513916 | Positive regulation of cell population proliferation (1) | 2.0 | 1.0 | 0.073069 |
| 561 | GO:0048511 | 0.251652 | Rhythmic process (1) | 3.0 | 1.0 | 0.067632 |
| 702 | GO:0048146 | 0.179210 | Positive regulation of fibroblast proliferation (1) | 0.0 | 1.0 | 0.067004 |
| 403 | GO:0033628 | 0.059919 | Regulation of cell adhesion mediated by integrin (1) | 1.0 | 1.0 | 0.061975 |
| 885 | GO:1900180 | 0.205300 | Regulation of protein localization to nucleus (1) | 1.0 | 1.0 | 0.060202 |
| 228 | GO:0006508 | 0.347532 | Proteolysis (1) | 4.0 | 1.0 | 0.056727 |
| 560 | GO:0043473 | 0.188137 | Pigmentation (1) | 2.0 | 1.0 | 0.036612 |
| 777 | GO:0050680 | 0.103322 | Negative regulation of epithelial cell proliferation (1) | 2.0 | 1.0 | 0.036383 |
| 751 | GO:0046034 | 0.094236 | Atp metabolic process (1) | 2.0 | 1.0 | 0.034832 |
| 591 | GO:0010941 | 0.705559 | Regulation of cell death (1) | 5.0 | 1.0 | 0.026616 |
| 818 | GO:0010243 | 0.557617 | Response to organonitrogen compound (1) | 4.0 | 1.0 | 0.022040 |
| 724 | GO:0036473 | 0.154959 | Cell death in response to oxidative stress (1) | 2.0 | 1.0 | 0.021429 |
| 153 | GO:0050776 | 0.424435 | Regulation of immune response (1) | 4.0 | 1.0 | 0.011047 |
| 521 | GO:0035051 | 0.187551 | Cardiocyte differentiation (1) | 2.0 | 1.0 | 0.003905 |
| 194 | GO:0006355 | 0.544373 | Regulation of transcription, dna-templated (1) | 4.0 | 1.0 | 0.003607 |
| 622 | GO:0043086 | 0.212387 | Negative regulation of catalytic activity (1) | 3.0 | 1.0 | -0.003920 |
| 494 | GO:0060840 | 0.094489 | Artery development (1) | 2.0 | 1.0 | -0.013518 |
| 936 | GO:0051000 | 0.059868 | Positive regulation of nitric-oxide synthase activity (1) | 0.0 | 1.0 | -0.017649 |
| 382 | GO:0030041 | 0.093571 | Actin filament polymerization (1) | 2.0 | 1.0 | -0.024295 |
| 712 | GO:0033002 | 0.280632 | Muscle cell proliferation (1) | 2.0 | 1.0 | -0.031117 |
| 750 | GO:0044281 | 0.305872 | Small molecule metabolic process (1) | 5.0 | 1.0 | -0.053702 |
| 257 | GO:0046488 | 0.135113 | Phosphatidylinositol metabolic process (1) | 2.0 | 1.0 | -0.069795 |
| 731 | GO:0031529 | 0.036028 | Ruffle organization (1) | 1.0 | 1.0 | -0.085835 |
| 469 | GO:0060173 | 0.072132 | Limb development (1) | 1.0 | 1.0 | -0.088184 |
| 296 | GO:0015031 | 0.292221 | Protein transport (1) | 4.0 | 1.0 | -0.098692 |
| 927 | GO:0042632 | 0.034870 | Cholesterol homeostasis (1) | 0.0 | 1.0 | -0.119705 |
| 78 | GO:0032760 | 0.100402 | Positive regulation of tumor necrosis factor production (1) | 0.0 | 1.0 | -0.132748 |
| 856 | GO:0019722 | 0.034146 | Calcium-mediated signaling (1) | 2.0 | 1.0 | -0.141431 |
| 288 | GO:0032940 | 0.217398 | Secretion by cell (1) | 5.0 | 1.0 | -0.147194 |
| 705 | GO:0051353 | 0.082787 | Positive regulation of oxidoreductase activity (1) | 1.0 | 1.0 | -0.158575 |
| 176 | GO:0044262 | 0.129234 | Cellular carbohydrate metabolic process (1) | 3.0 | 1.0 | -0.190099 |
| 180 | GO:0019318 | 0.046251 | Hexose metabolic process (1) | 2.0 | 1.0 | -0.211363 |
| 440 | GO:0030512 | 0.045444 | Negative regulation of transforming growth factor beta receptor signaling pathway (1) | 0.0 | 1.0 | -0.229806 |
| 331 | GO:0071887 | 0.072964 | Leukocyte apoptotic process (1) | 2.0 | 1.0 | -0.295526 |
| 823 | GO:0045471 | 0.068117 | Response to ethanol (1) | 1.0 | 1.0 | -0.639920 |
| 232 | GO:0010951 | 0.025246 | Negative regulation of endopeptidase activity (1) | 2.0 | 1.0 | -0.917276 |
names2 = list(probabilities_mod.loc[probabilities_mod["predictions"] ==1].loc[probabilities_mod["layer_number"] <=7].sort_values(by=["delta_logits"], ascending=False)["Name"].head(30))
terms2 = list(probabilities_mod.loc[probabilities_mod["predictions"] ==1].loc[probabilities_mod["layer_number"] <=7].sort_values(by=["delta_logits"], ascending=False)["GO_term"].head(30))
logits2 = list(probabilities_mod.loc[probabilities_mod["predictions"] ==1].loc[probabilities_mod["layer_number"] <=7].sort_values(by=["delta_logits"], ascending=False)["delta_logits"].head(30))
names2 = [x[:-4] for x in names2] for i in range(0,len(names2)):
print(terms2[i],names2[i],logits2[i])GO:0031047 Gene silencing by rna 2.192457619336144
GO:0031648 Protein destabilization 2.092833916210919
GO:0071900 Regulation of protein serine/threonine kinase activity 1.944569179670069
GO:0000423 Mitophagy 1.8800065969407627
GO:0048266 Behavioral response to pain 1.8570307139212263
GO:0090630 Activation of gtpase activity 1.793943480641404
GO:0045727 Positive regulation of translation 1.7460704310285706
GO:0018108 Peptidyl-tyrosine phosphorylation 1.6728257190053135
GO:0043244 Regulation of protein-containing complex disassembly 1.6460980615310405
GO:0006612 Protein targeting to membrane 1.6268405228374492
GO:0006469 Negative regulation of protein kinase activity 1.5937943848967007
GO:0034976 Response to endoplasmic reticulum stress 1.575980420814227
GO:0071353 Cellular response to interleukin-4 1.5752555355925209
GO:0033554 Cellular response to stress 1.573975479674923
GO:0033993 Response to lipid 1.5515734212063519
GO:0001817 Regulation of cytokine production 1.5347154822720621
GO:0007026 Negative regulation of microtubule depolymerization 1.5337813437727923
GO:0007565 Female pregnancy 1.4788810435976616
GO:0046777 Protein autophosphorylation 1.453821209026271
GO:0060179 Male mating behavior 1.4409006464912832
GO:0000422 Autophagy of mitochondrion 1.4098807478143418
GO:0030216 Keratinocyte differentiation 1.406529668352229
GO:0048812 Neuron projection morphogenesis 1.3850039160074457
GO:0001553 Luteinization 1.3733255324441331
GO:0002718 Regulation of cytokine production involved in immune response 1.3728274508963876
GO:0042060 Wound healing 1.325910525701464
GO:0060632 Regulation of microtubule-based movement 1.3061039503079361
GO:0065003 Protein-containing complex assembly 1.304383769510548
GO:0060020 Bergmann glial cell differentiation 1.2886961413918279
GO:1902459 Positive regulation of stem cell population maintenance 1.2787216247479065
# import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# set font
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = 'Roboto'
# set the style of the axes and the text color
plt.rcParams['axes.edgecolor']='#333F4B'
plt.rcParams['axes.linewidth']=0.8
plt.rcParams['xtick.color']='#333F4B'
plt.rcParams['ytick.color']='#333F4B'
plt.rcParams['text.color']='#333F4B'
# create some fake data
percentages = pd.Series(logits2,
index=names2)
df = pd.DataFrame({'percentage' : percentages})
df = df.sort_values(by='percentage')
# we first need a numeric placeholder for the y axis
my_range=list(range(1,len(df.index)+1))
fig, ax = plt.subplots(figsize=(4,17))
# create for each expense type an horizontal line that starts at x = 0 with the length
# represented by the specific expense percentage value.
plt.hlines(y=my_range, xmin=0, xmax=df['percentage'], color='#208EA3', alpha=0.2, linewidth=14)
# create for each expense type a dot at the level of the expense percentage value
plt.plot(df['percentage'], my_range, "o", markersize=14, color='#208EA3', alpha=0.8)
# set labels
ax.set_xlabel(' Δlogit', fontsize=25, fontweight='black', color = '#36382E')
ax.set_ylabel('')
ax.set_facecolor(color="white")
ax.set_alpha(1)
# set axis
ax.tick_params(axis='both', which='major', labelsize=30)
plt.yticks(my_range, df.index)
# add an horizonal label for the y axis
fig.text(-0.58, 0.862, 'MoA (GO terms)', fontsize=27, fontweight='black', color = '#36382E')
fig.text(0.2, 0.9, selected_drug_u_name.capitalize(), fontsize=30, fontweight='black', color = '#36382E')
# change the style of the axis spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_bounds((1, len(my_range)))
ax.set_xlim(0,max(logits2)+0.1)
ax.spines['left'].set_position(('outward', 8))
ax.spines['bottom'].set_position(('outward', 5))
plt.savefig(resultsdir+selected_drug_u_name+'_top_terms.png', dpi=300, bbox_inches='tight')
For known drug…
display(combobox)selected_drug_name = combobox.result# LOS LOGITS DE TEST!!
train_drug_logs = pd.DataFrame(platt_matrix.loc[:,selected_drug_name]).reset_index()
train_drug_logs.columns = ["GO_term","probability"]
train_drug_logs = train_drug_logs.merge(real_go_info_svm, on="GO_term")train_drug_logs.loc[train_drug_logs["layer_number"] <=3].sort_values(by=["probability"], ascending=False).head(30)| GO_term | probability | Name | layer_number | |
|---|---|---|---|---|
| 423 | GO:1902533 | 0.825532 | Positive regulation of intracellular signal transduction (1) | 2.0 |
| 99 | GO:0001934 | 0.823688 | Positive regulation of protein phosphorylation (1) | 3.0 |
| 633 | GO:0051301 | 0.817491 | Cell division (1) | 2.0 |
| 224 | GO:0046777 | 0.780375 | Protein autophosphorylation (1) | 1.0 |
| 8 | GO:0000165 | 0.778813 | Mapk cascade (1) | 3.0 |
| 624 | GO:0010628 | 0.741437 | Positive regulation of gene expression (1) | 3.0 |
| 253 | GO:0043552 | 0.735601 | Positive regulation of phosphatidylinositol 3-kinase activity (1) | 0.0 |
| 437 | GO:0007169 | 0.646597 | Transmembrane receptor protein tyrosine kinase signaling pathway (1) | 3.0 |
| 653 | GO:0072593 | 0.631785 | Reactive oxygen species metabolic process (1) | 3.0 |
| 894 | GO:0048017 | 0.609803 | Inositol lipid-mediated signaling (1) | 1.0 |
| 578 | GO:2000379 | 0.603184 | Positive regulation of reactive oxygen species metabolic process (1) | 1.0 |
| 24 | GO:0007346 | 0.596523 | Regulation of mitotic cell cycle (1) | 3.0 |
| 573 | GO:0010629 | 0.592396 | Negative regulation of gene expression (1) | 3.0 |
| 908 | GO:0051899 | 0.591286 | Membrane depolarization (1) | 2.0 |
| 906 | GO:0043549 | 0.586633 | Regulation of kinase activity (1) | 3.0 |
| 105 | GO:0045860 | 0.585544 | Positive regulation of protein kinase activity (1) | 2.0 |
| 12 | GO:0070374 | 0.584467 | Positive regulation of erk1 and erk2 cascade (1) | 0.0 |
| 74 | GO:0001817 | 0.527795 | Regulation of cytokine production (1) | 3.0 |
| 44 | GO:0048812 | 0.526118 | Neuron projection morphogenesis (1) | 3.0 |
| 853 | GO:0038083 | 0.500000 | Peptidyl-tyrosine autophosphorylation (1) | 0.0 |
| 223 | GO:0018108 | 0.493038 | Peptidyl-tyrosine phosphorylation (1) | 3.0 |
| 9 | GO:0043408 | 0.472479 | Regulation of mapk cascade (1) | 2.0 |
| 570 | GO:0045597 | 0.464440 | Positive regulation of cell differentiation (1) | 3.0 |
| 333 | GO:1904019 | 0.457451 | Epithelial cell apoptotic process (1) | 1.0 |
| 702 | GO:0048146 | 0.440487 | Positive regulation of fibroblast proliferation (1) | 0.0 |
| 80 | GO:0010575 | 0.438910 | Positive regulation of vascular endothelial growth factor production (1) | 0.0 |
| 106 | GO:0071900 | 0.437003 | Regulation of protein serine/threonine kinase activity (1) | 2.0 |
| 141 | GO:0050900 | 0.435245 | Leukocyte migration (1) | 3.0 |
| 10 | GO:0051403 | 0.429898 | Stress-activated mapk cascade (1) | 2.0 |
| 11 | GO:0043406 | 0.426305 | Positive regulation of map kinase activity (1) | 1.0 |
# # For known drugs
len((set(train_drug_logs.loc[train_drug_logs["layer_number"] <=3].sort_values(by=["probability"], ascending=False).head(30)["GO_term"])).intersection(set(pd.DataFrame(compounds_GOterms_matches[selected_drug_name])[1])))30
ax = sns.boxplot(x=slim_matrix_single_neuron.loc[train_drug_logs["GO_term"],selected_drug_name], y=train_drug_logs.set_index("GO_term")["probability"], data=plot,showfliers=True )
# same as before
sum_annotations = slim_matrix_single_neuron.T.sum()/slim_matrix_single_neuron.shape[1]
logits_apriori= np.log(sum_annotations/(1-sum_annotations))logits_apost= np.log(train_drug_logs["probability"]/(1-train_drug_logs["probability"]))
delta_logits = logits_apost.to_numpy()-logits_apriori.to_numpy()
delta_logits_df = pd.DataFrame(delta_logits)
delta_logits_df.columns = ["delta_logits"]
train_drug_mod = train_drug_logs.merge(delta_logits_df, left_index=True,right_index=True)train_drug_mod.loc[train_drug_mod["layer_number"] <=3].sort_values(by=["delta_logits"], ascending=False).head(30)| GO_term | probability | Name | layer_number | delta_logits | |
|---|---|---|---|---|---|
| 578 | GO:2000379 | 0.603184 | Positive regulation of reactive oxygen species metabolic process (1) | 1.0 | 2.770126 |
| 253 | GO:0043552 | 0.735601 | Positive regulation of phosphatidylinositol 3-kinase activity (1) | 0.0 | 2.707570 |
| 80 | GO:0010575 | 0.438910 | Positive regulation of vascular endothelial growth factor production (1) | 0.0 | 2.282492 |
| 633 | GO:0051301 | 0.817491 | Cell division (1) | 2.0 | 2.245231 |
| 224 | GO:0046777 | 0.780375 | Protein autophosphorylation (1) | 1.0 | 2.053782 |
| 423 | GO:1902533 | 0.825532 | Positive regulation of intracellular signal transduction (1) | 2.0 | 2.014408 |
| 458 | GO:0035025 | 0.374269 | Positive regulation of rho protein signal transduction (1) | 0.0 | 1.952270 |
| 848 | GO:0071670 | 0.345148 | Smooth muscle cell chemotaxis (1) | 0.0 | 1.887644 |
| 348 | GO:0006939 | 0.412178 | Smooth muscle contraction (1) | 2.0 | 1.842256 |
| 350 | GO:0045987 | 0.348868 | Positive regulation of smooth muscle contraction (1) | 1.0 | 1.783402 |
| 908 | GO:0051899 | 0.591286 | Membrane depolarization (1) | 2.0 | 1.675960 |
| 653 | GO:0072593 | 0.631785 | Reactive oxygen species metabolic process (1) | 3.0 | 1.650121 |
| 115 | GO:0060312 | 0.275354 | Regulation of blood vessel remodeling (1) | 0.0 | 1.625762 |
| 926 | GO:0060020 | 0.369360 | Bergmann glial cell differentiation (1) | 0.0 | 1.614859 |
| 923 | GO:0048170 | 0.295742 | Positive regulation of long-term neuronal synaptic plasticity (1) | 0.0 | 1.598558 |
| 99 | GO:0001934 | 0.823688 | Positive regulation of protein phosphorylation (1) | 3.0 | 1.593723 |
| 853 | GO:0038083 | 0.500000 | Peptidyl-tyrosine autophosphorylation (1) | 0.0 | 1.588712 |
| 713 | GO:0035726 | 0.210551 | Common myeloid progenitor cell proliferation (1) | 0.0 | 1.577983 |
| 445 | GO:0048008 | 0.414654 | Platelet-derived growth factor receptor signaling pathway (1) | 1.0 | 1.552362 |
| 857 | GO:0035584 | 0.298388 | Calcium-mediated signaling using intracellular calcium source (1) | 0.0 | 1.496390 |
| 333 | GO:1904019 | 0.457451 | Epithelial cell apoptotic process (1) | 1.0 | 1.481165 |
| 933 | GO:0051150 | 0.337253 | Regulation of smooth muscle cell differentiation (1) | 1.0 | 1.474264 |
| 352 | GO:0014827 | 0.254529 | Intestine smooth muscle contraction (1) | 0.0 | 1.453480 |
| 814 | GO:0090037 | 0.235572 | Positive regulation of protein kinase c signaling (1) | 0.0 | 1.416277 |
| 894 | GO:0048017 | 0.609803 | Inositol lipid-mediated signaling (1) | 1.0 | 1.399588 |
| 506 | GO:0007286 | 0.240113 | Spermatid development (1) | 1.0 | 1.376021 |
| 742 | GO:0035733 | 0.239304 | Hepatic stellate cell activation (1) | 0.0 | 1.371582 |
| 10 | GO:0051403 | 0.429898 | Stress-activated mapk cascade (1) | 2.0 | 1.369506 |
| 702 | GO:0048146 | 0.440487 | Positive regulation of fibroblast proliferation (1) | 0.0 | 1.349525 |
| 782 | GO:1902042 | 0.289337 | Negative regulation of extrinsic apoptotic signaling pathway via death domain receptors (1) | 0.0 | 1.347889 |
ax = sns.boxplot(x=slim_matrix_single_neuron.loc[train_drug_mod["GO_term"],selected_drug_name], y=train_drug_mod.set_index("GO_term")["delta_logits"], data=plot,showfliers=True)
SVM GO TERM 2D representation
from sklearn.manifold import TSNE
import plotly.express as pxChoose go to study…
display(combobox_go)selected_goterm = combobox_go.resultreal_go_info[real_go_info["GO_term"]==selected_goterm+"_1"]| GO_term | Name | layer_number | |
|---|---|---|---|
| 4338 | GO:0071353_1 | Cellular response to interleukin-4 (1) | 1.0 |
list_nodes = []
for i in range(1,7):
list_nodes.append(selected_goterm+"_"+str(i))
score = attribution_data_annotated.loc[list_nodes].T
score_mod = score.divide(score.std()).fillna(0)
annotations =slim_matrix_single_neuron.loc[selected_goterm,]
y_predicted = models_svm[selected_goterm].predict(score_mod.astype(float))Plot SVM
View statistics of GOterm
“Perfect” model (with train data)
auc = metrics.roc_auc_score(annotations, models_svm[selected_goterm].decision_function(score_mod.astype(float)))
cnf_matrix = metrics.confusion_matrix(annotations,y_predicted)
print(cnf_matrix)
print("Accuracy:",metrics.accuracy_score(annotations, y_predicted))
print("Precision:",metrics.precision_score(annotations,y_predicted)) # TP / (TP+FP)
print("Recall:",metrics.recall_score(annotations, y_predicted)) #TP / (TP+FN)
print("AUC with score:",auc) [[206 6]
[ 2 16]]
Accuracy: 0.9652173913043478
Precision: 0.7272727272727273
Recall: 0.8888888888888888
AUC with score: 0.9855870020964361
TN - FP
FN - TP
En mi opinion interesa mucho el precision, prefiero que haya menos FP no??
Test statistics…
auc = metrics.roc_auc_score(slim_matrix_single_neuron.loc[selected_goterm], platt_matrix.loc[selected_goterm])
cnf_matrix = metrics.confusion_matrix(slim_matrix_single_neuron.loc[selected_goterm], preds_svm_matrix.loc[selected_goterm])
print(cnf_matrix)
print("Accuracy:",metrics.accuracy_score(slim_matrix_single_neuron.loc[selected_goterm], preds_svm_matrix.loc[selected_goterm]))
print("Precision:",metrics.precision_score(slim_matrix_single_neuron.loc[selected_goterm], preds_svm_matrix.loc[selected_goterm]))
print("Recall:",metrics.recall_score(slim_matrix_single_neuron.loc[selected_goterm], preds_svm_matrix.loc[selected_goterm])) #TP / (TP+FN)
print("AUC with score:",auc) #TP / (TP+FN)[[203 9]
[ 4 14]]
Accuracy: 0.9434782608695652
Precision: 0.6086956521739131
Recall: 0.7777777777777778
AUC with score: 0.9095911949685536
import colorlover as cl
matrix = metrics.confusion_matrix(annotations,y_predicted)
tn, fp, fn, tp = matrix.ravel()
values = [tp, fn, fp, tn]
label_text = ["True Positive", "False Negative", "False Positive", "True Negative"]
labels = ["<b>TP</b>", "<b>FN</b>", "<b>FP</b>", "<b>TN</b>"]
blue = cl.flipper()["seq"]["9"]["Blues"]
red = cl.flipper()["seq"]["9"]["Reds"]
colors = ["#ff3700","#FFA0A0", "#CCE9FF", "#0b8bff"]
trace0 = go.Pie(
labels=label_text,
values=values,
hoverinfo="label+value+percent",
textinfo="text+value",
text=labels,
sort=False,
marker=dict(colors=colors),
insidetextfont={"color": "#36382E"},
rotation=90,
)
layout = go.Layout(
title=dict(text="Confusion Matrix",
x=0.3,
y=0.8,
font=dict(size=14),
xanchor='center',
yanchor='top'),
#margin=dict(l=50, r=50, t=100, b=10),
legend=dict(font={"color": "#36382E"}, orientation="h",x=0.1, y=-0.03),
# plot_bgcolor="#282b38",
# paper_bgcolor="#282b38",
font=dict(family='Roboto',color= "#36382E"),
)
data = [trace0]
figure = go.Figure(data=data, layout=layout)
figurey_test=annotations
decision_test=y_predicted
fpr, tpr, threshold = metrics.roc_curve(y_test, decision_test)
# AUC Score
auc_score = metrics.roc_auc_score(y_true=y_test, y_score=decision_test)
trace0 = go.Scatter(
x=fpr, y=tpr, mode="lines", name="Test Data", marker={"color": "#ff3700"}
)
layout = go.Layout(
title=dict(text=f"ROC Curve (AUC = {auc_score:.3f})",
x=0.6,
y=0.5,
font=dict(size=20)
),
xaxis=dict(title="False Positive Rate", gridcolor="white"),
yaxis=dict(title="True Positive Rate", gridcolor="white"),
legend=dict(x=0, y=1.05, orientation="h"),
margin=dict(l=100, r=10, t=25, b=40),
# plot_bgcolor="#282b38",
# paper_bgcolor="#282b38",
font=dict(family='Roboto',color= "#36382E"),
)
data = [trace0]
figure = go.Figure(data=data, layout=layout)
figurePlot SVM with unknown labels
Voronoi Tessellation
What is a Voronoi Tessellation? Given a set P := {p1, …, pn} of sites, a Voronoi Tessellation is a subdivision of the space into n cells, one for each site in P, with the property that a point q lies in the cell corresponding to a site pi iff d(pi, q) < d(pj, q) for i distinct from j. The segments in a Voronoi Tessellation correspond to all points in the plane equidistant to the two nearest sites. Voronoi Tessellations have applications in computer science.
https://stackoverflow.com/questions/61225052/svm-plot-decision-surface-when-working-with-more-than-2-features
tsne = TSNE(n_components=2, verbose=0,
init="pca",
perplexity=30,
random_state=123
)
z = tsne.fit_transform(score_mod.astype(float)) C:\Users\ksada\Anaconda3\envs\SparseGO\lib\site-packages\sklearn\manifold\_t_sne.py:790: FutureWarning:
The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
C:\Users\ksada\Anaconda3\envs\SparseGO\lib\site-packages\sklearn\manifold\_t_sne.py:982: FutureWarning:
The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.
list_nodes = list(models_svm[selected_goterm].feature_names_in_) # Extract the feature names from the model (those are the attributions we need)
score_unknown = attribution_data_all.loc[list_nodes,unknown].T
score_unknown_mod = score_unknown.divide(score.std()).fillna(0) # normalizey_unknown = np.full(score_unknown_mod.shape[0],2) # 2=unknown MOA
y_pred_unknown = models_svm[selected_goterm].predict(score_unknown_mod.astype(float))# join scores and annotations from known and unknown drugs
all_score = pd.concat([score_mod,score_unknown_mod])
all_y = np.concatenate((annotations,y_unknown)) # 2=unknown MOAPlot T-SNE SVM
from sklearn.neighbors._classification import KNeighborsClassifier
# https://github.com/plotly/dash-sample-apps/blob/main/apps/dash-svm/utils/dash_reusable_components.py
z = tsne.fit_transform(all_score.astype(float))
df = pd.DataFrame()
df["y"] = all_y
df["comp-1"] = z[:,0]
df["comp-2"] = z[:,1]
df["name"] =list(all_score.index)
df = df.sort_values(by=['y'])
df["y"] = df["y"].astype(str)
X,y = all_score.astype(float), all_y
y_predicted = models_svm[selected_goterm].predict(X)
resolution = 300 # 100x100 background pixels
X2d_xmin, X2d_xmax = np.min(z[:,0])-1, np.max(z[:,0])+1
X2d_ymin, X2d_ymax = np.min(z[:,1])-1, np.max(z[:,1])+1
xx, yy = np.meshgrid(np.linspace(X2d_xmin, X2d_xmax, resolution), np.linspace(X2d_ymin, X2d_ymax, resolution))
# approximate Voronoi tesselation on resolution x resolution grid using 1-NN
background_model = KNeighborsClassifier(n_neighbors=1).fit(z, y_predicted)
voronoiBackground = background_model.predict(np.c_[xx.ravel(), yy.ravel()])
voronoiBackground = voronoiBackground.reshape((resolution, resolution))C:\Users\ksada\Anaconda3\envs\SparseGO\lib\site-packages\sklearn\manifold\_t_sne.py:790: FutureWarning:
The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
C:\Users\ksada\Anaconda3\envs\SparseGO\lib\site-packages\sklearn\manifold\_t_sne.py:982: FutureWarning:
The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.
C:\Users\ksada\Anaconda3\envs\SparseGO\lib\site-packages\sklearn\neighbors\_classification.py:228: FutureWarning:
Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
go_name=real_go_info[real_go_info["GO_term"]==selected_goterm+"_1"]["Name"].values[0][:-4]
go_name'Cellular response to interleukin-4'
bright_cscale = [[0, "#0b8bff"], [0.5, "#ff3700"],[1, "#36382E"]]
new_cscale = [[0, "#CCE9FF"], [1, "#FFA0A0"]]
trace0 = go.Contour(
x=xx.flatten(),
y=yy.flatten(),
z=voronoiBackground.flatten(),
hoverinfo="none",
showscale=False,
contours=dict(showlines=False),
colorscale=new_cscale,
opacity=0.9,
)
trace1 = go.Contour(
x=xx.flatten(),
y=yy.flatten(),
z=voronoiBackground.flatten(),
showscale=False,
hoverinfo="none",
colorscale=new_cscale,
line=dict(color="#ff3700"),
)
trace2 = go.Scatter(
x=df["comp-1"],
y=df["comp-2"],
mode="markers",
text=df["name"].to_numpy(),
marker=dict(size=7, color=df["y"].to_numpy(int),colorscale=bright_cscale),
showlegend=False
)
legend1 = go.Scatter(
x=[None],
y=[None],
mode="markers",
name="Not annotated to<br>"+selected_goterm,
marker=dict(size=7, color="#0b8bff",symbol='circle'),
)
legend2 = go.Scatter(
x=[None],
y=[None],
mode="markers",
name="Drug annotated to<br>"+selected_goterm,
marker=dict(size=7, color="#ff3700",symbol='circle'),
)
legend3 = go.Scatter(
x=[None],
y=[None],
mode="markers",
name="Unknown MOA<br>annotations",
marker=dict(size=7, color="#36382E",symbol='circle'),
)
layout = go.Layout(
title=dict(text="<b>"+selected_goterm+"</b> "+go_name,
x=0.5,
y=0.92,
font=dict(size=18),
xanchor='center',
yanchor='top'),
xaxis=dict(ticks="", showticklabels=False, showgrid=False, zeroline=False),
yaxis=dict(ticks="", showticklabels=False, showgrid=False, zeroline=False),
yaxis_range=[min(yy.flatten()),max(yy.flatten())],
xaxis_range=[min(xx.flatten()),max(xx.flatten())],
legend=dict(x=0, y=0, orientation="h",font=dict(size=14)),
paper_bgcolor='rgba(0,0,0,0)',
width=600, height=800,
font=dict(family='Roboto',color= "#36382E",size=15)
)
data = [trace0,trace1,trace2,legend2,legend1,legend3]
figure = go.Figure(data=data,layout=layout)
figure